This project was carried out in 2nd year of engineering (BAC+4) at ESILV for the Deep Learning exam.
The objective of this project is to determine whether the patient presents a cardiac pathology according to his physical characteristics.
the dataset is described as follows:
The ariables to be predicted are : Absence (1) or presence (2) of heart disease
from __future__ import absolute_import , division , print_function , unicode_literals
3 # TensorFlow and tf. keras
import tensorflow as tf
from tensorflow import keras
from random import seed
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from tensorflow.keras.optimizers import Adagrad, Adam, RMSprop, SGD
from sklearn.model_selection import train_test_split
from keras.callbacks import ModelCheckpoint
from keras.callbacks import EarlyStopping
from keras.models import load_model
from keras.layers import BatchNormalization
from tensorflow.keras.utils import to_categorical
from sklearn.utils import shuffle
from tensorflow.keras import regularizers
from sklearn.metrics import accuracy_score
# create dataset
There is 40 datasets (with 216 rows) of heart disease information. The exam is split in two parts :
df=pd.DataFrame()
for i in range(1,36):
globals()[f"df_{i}"] = pd.read_csv(f"heart_train_{i}.txt", sep=" ",header=None)
df = pd.concat([df, globals()[f"df_{i}"]])
df
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 50.0 | 0.0 | 2.0 | 120.0 | 244.0 | 0.0 | 0.0 | 162.0 | 0.0 | 1.1 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 56.0 | 1.0 | 4.0 | 130.0 | 283.0 | 1.0 | 2.0 | 103.0 | 1.0 | 1.6 | 3.0 | 0.0 | 7.0 | 2 |
| 2 | 44.0 | 1.0 | 3.0 | 120.0 | 226.0 | 0.0 | 0.0 | 169.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 3 | 55.0 | 1.0 | 4.0 | 160.0 | 289.0 | 0.0 | 2.0 | 145.0 | 1.0 | 0.8 | 2.0 | 1.0 | 7.0 | 2 |
| 4 | 54.0 | 1.0 | 2.0 | 108.0 | 309.0 | 0.0 | 0.0 | 156.0 | 0.0 | 0.0 | 1.0 | 0.0 | 7.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | 44.0 | 0.0 | 3.0 | 108.0 | 141.0 | 0.0 | 0.0 | 175.0 | 0.0 | 0.6 | 2.0 | 0.0 | 3.0 | 1 |
| 212 | 50.0 | 1.0 | 3.0 | 129.0 | 196.0 | 0.0 | 0.0 | 163.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 213 | 63.0 | 0.0 | 4.0 | 150.0 | 407.0 | 0.0 | 2.0 | 154.0 | 0.0 | 4.0 | 2.0 | 3.0 | 7.0 | 2 |
| 214 | 50.0 | 1.0 | 4.0 | 150.0 | 243.0 | 0.0 | 2.0 | 128.0 | 0.0 | 2.6 | 2.0 | 0.0 | 7.0 | 2 |
| 215 | 45.0 | 0.0 | 4.0 | 138.0 | 236.0 | 0.0 | 2.0 | 152.0 | 1.0 | 0.2 | 2.0 | 0.0 | 3.0 | 1 |
7560 rows × 14 columns
df=df.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
df
| age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 50.0 | 0.0 | 2.0 | 120.0 | 244.0 | 0.0 | 0.0 | 162.0 | 0.0 | 1.1 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 56.0 | 1.0 | 4.0 | 130.0 | 283.0 | 1.0 | 2.0 | 103.0 | 1.0 | 1.6 | 3.0 | 0.0 | 7.0 | 2 |
| 2 | 44.0 | 1.0 | 3.0 | 120.0 | 226.0 | 0.0 | 0.0 | 169.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 3 | 55.0 | 1.0 | 4.0 | 160.0 | 289.0 | 0.0 | 2.0 | 145.0 | 1.0 | 0.8 | 2.0 | 1.0 | 7.0 | 2 |
| 4 | 54.0 | 1.0 | 2.0 | 108.0 | 309.0 | 0.0 | 0.0 | 156.0 | 0.0 | 0.0 | 1.0 | 0.0 | 7.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | 44.0 | 0.0 | 3.0 | 108.0 | 141.0 | 0.0 | 0.0 | 175.0 | 0.0 | 0.6 | 2.0 | 0.0 | 3.0 | 1 |
| 212 | 50.0 | 1.0 | 3.0 | 129.0 | 196.0 | 0.0 | 0.0 | 163.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 213 | 63.0 | 0.0 | 4.0 | 150.0 | 407.0 | 0.0 | 2.0 | 154.0 | 0.0 | 4.0 | 2.0 | 3.0 | 7.0 | 2 |
| 214 | 50.0 | 1.0 | 4.0 | 150.0 | 243.0 | 0.0 | 2.0 | 128.0 | 0.0 | 2.6 | 2.0 | 0.0 | 7.0 | 2 |
| 215 | 45.0 | 0.0 | 4.0 | 138.0 | 236.0 | 0.0 | 2.0 | 152.0 | 1.0 | 0.2 | 2.0 | 0.0 | 3.0 | 1 |
7560 rows × 14 columns
df.columns
Index(['age', 'sex', 'chest_pain', 'blood_pressure', 'cholestoral',
'blood_sugar', 'electrocardiographic', 'max_heart_rate', 'exercise',
'oldpeak', 'peak_exer', 'nb_vessel', 'defect', 'disease'],
dtype='object')
df.to_csv("heart_train.txt",sep=" ", index=False)
#we save the dataset
df= pd.read_csv(f"heart_train.txt", sep=" ")
df
| Unnamed: 0 | age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 50.0 | 0.0 | 2.0 | 120.0 | 244.0 | 0.0 | 0.0 | 162.0 | 0.0 | 1.1 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 1 | 56.0 | 1.0 | 4.0 | 130.0 | 283.0 | 1.0 | 2.0 | 103.0 | 1.0 | 1.6 | 3.0 | 0.0 | 7.0 | 2 |
| 2 | 2 | 44.0 | 1.0 | 3.0 | 120.0 | 226.0 | 0.0 | 0.0 | 169.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 3 | 3 | 55.0 | 1.0 | 4.0 | 160.0 | 289.0 | 0.0 | 2.0 | 145.0 | 1.0 | 0.8 | 2.0 | 1.0 | 7.0 | 2 |
| 4 | 4 | 54.0 | 1.0 | 2.0 | 108.0 | 309.0 | 0.0 | 0.0 | 156.0 | 0.0 | 0.0 | 1.0 | 0.0 | 7.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7555 | 211 | 44.0 | 0.0 | 3.0 | 108.0 | 141.0 | 0.0 | 0.0 | 175.0 | 0.0 | 0.6 | 2.0 | 0.0 | 3.0 | 1 |
| 7556 | 212 | 50.0 | 1.0 | 3.0 | 129.0 | 196.0 | 0.0 | 0.0 | 163.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 7557 | 213 | 63.0 | 0.0 | 4.0 | 150.0 | 407.0 | 0.0 | 2.0 | 154.0 | 0.0 | 4.0 | 2.0 | 3.0 | 7.0 | 2 |
| 7558 | 214 | 50.0 | 1.0 | 4.0 | 150.0 | 243.0 | 0.0 | 2.0 | 128.0 | 0.0 | 2.6 | 2.0 | 0.0 | 7.0 | 2 |
| 7559 | 215 | 45.0 | 0.0 | 4.0 | 138.0 | 236.0 | 0.0 | 2.0 | 152.0 | 1.0 | 0.2 | 2.0 | 0.0 | 3.0 | 1 |
7560 rows × 15 columns
validation=pd.DataFrame()
for i in range(36,41):
globals()[f"df_{i}"] = pd.read_csv(f"heart_train_{i}.txt", sep=" ",header=None)
validation = pd.concat([validation, globals()[f"df_{i}"]])
validation
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 47.0 | 1.0 | 4.0 | 112.0 | 204.0 | 0.0 | 0.0 | 143.0 | 0.0 | 0.1 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 62.0 | 0.0 | 4.0 | 138.0 | 294.0 | 1.0 | 0.0 | 106.0 | 0.0 | 1.9 | 2.0 | 3.0 | 3.0 | 2 |
| 2 | 55.0 | 1.0 | 4.0 | 160.0 | 289.0 | 0.0 | 2.0 | 145.0 | 1.0 | 0.8 | 2.0 | 1.0 | 7.0 | 2 |
| 3 | 71.0 | 0.0 | 4.0 | 112.0 | 149.0 | 0.0 | 0.0 | 125.0 | 0.0 | 1.6 | 2.0 | 0.0 | 3.0 | 1 |
| 4 | 67.0 | 0.0 | 3.0 | 115.0 | 564.0 | 0.0 | 2.0 | 160.0 | 0.0 | 1.6 | 2.0 | 0.0 | 7.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | 56.0 | 1.0 | 1.0 | 120.0 | 193.0 | 0.0 | 2.0 | 162.0 | 0.0 | 1.9 | 2.0 | 0.0 | 7.0 | 1 |
| 212 | 49.0 | 0.0 | 2.0 | 134.0 | 271.0 | 0.0 | 0.0 | 162.0 | 0.0 | 0.0 | 2.0 | 0.0 | 3.0 | 1 |
| 213 | 54.0 | 0.0 | 3.0 | 110.0 | 214.0 | 0.0 | 0.0 | 158.0 | 0.0 | 1.6 | 2.0 | 0.0 | 3.0 | 1 |
| 214 | 41.0 | 0.0 | 2.0 | 130.0 | 204.0 | 0.0 | 2.0 | 172.0 | 0.0 | 1.4 | 1.0 | 0.0 | 3.0 | 1 |
| 215 | 66.0 | 0.0 | 4.0 | 178.0 | 228.0 | 1.0 | 0.0 | 165.0 | 1.0 | 1.0 | 2.0 | 2.0 | 7.0 | 2 |
1080 rows × 14 columns
validation=validation.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
validation
| age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 47.0 | 1.0 | 4.0 | 112.0 | 204.0 | 0.0 | 0.0 | 143.0 | 0.0 | 0.1 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 62.0 | 0.0 | 4.0 | 138.0 | 294.0 | 1.0 | 0.0 | 106.0 | 0.0 | 1.9 | 2.0 | 3.0 | 3.0 | 2 |
| 2 | 55.0 | 1.0 | 4.0 | 160.0 | 289.0 | 0.0 | 2.0 | 145.0 | 1.0 | 0.8 | 2.0 | 1.0 | 7.0 | 2 |
| 3 | 71.0 | 0.0 | 4.0 | 112.0 | 149.0 | 0.0 | 0.0 | 125.0 | 0.0 | 1.6 | 2.0 | 0.0 | 3.0 | 1 |
| 4 | 67.0 | 0.0 | 3.0 | 115.0 | 564.0 | 0.0 | 2.0 | 160.0 | 0.0 | 1.6 | 2.0 | 0.0 | 7.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | 56.0 | 1.0 | 1.0 | 120.0 | 193.0 | 0.0 | 2.0 | 162.0 | 0.0 | 1.9 | 2.0 | 0.0 | 7.0 | 1 |
| 212 | 49.0 | 0.0 | 2.0 | 134.0 | 271.0 | 0.0 | 0.0 | 162.0 | 0.0 | 0.0 | 2.0 | 0.0 | 3.0 | 1 |
| 213 | 54.0 | 0.0 | 3.0 | 110.0 | 214.0 | 0.0 | 0.0 | 158.0 | 0.0 | 1.6 | 2.0 | 0.0 | 3.0 | 1 |
| 214 | 41.0 | 0.0 | 2.0 | 130.0 | 204.0 | 0.0 | 2.0 | 172.0 | 0.0 | 1.4 | 1.0 | 0.0 | 3.0 | 1 |
| 215 | 66.0 | 0.0 | 4.0 | 178.0 | 228.0 | 1.0 | 0.0 | 165.0 | 1.0 | 1.0 | 2.0 | 2.0 | 7.0 | 2 |
1080 rows × 14 columns
validation.to_csv("heart_validation.txt",sep=" ", index=False)
#we save the dataset
small_df = pd.read_csv(f"heart_train_33.txt", sep=" ",header=None)
small_df=small_df.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
small_df
| age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 67.0 | 0.0 | 4.0 | 106.0 | 223.0 | 0.0 | 0.0 | 142.0 | 0.0 | 0.3 | 1.0 | 2.0 | 3.0 | 1 |
| 1 | 52.0 | 1.0 | 2.0 | 134.0 | 201.0 | 0.0 | 0.0 | 158.0 | 0.0 | 0.8 | 1.0 | 1.0 | 3.0 | 1 |
| 2 | 50.0 | 0.0 | 2.0 | 120.0 | 244.0 | 0.0 | 0.0 | 162.0 | 0.0 | 1.1 | 1.0 | 0.0 | 3.0 | 1 |
| 3 | 57.0 | 1.0 | 4.0 | 165.0 | 289.0 | 1.0 | 2.0 | 124.0 | 0.0 | 1.0 | 2.0 | 3.0 | 7.0 | 2 |
| 4 | 48.0 | 1.0 | 3.0 | 124.0 | 255.0 | 1.0 | 0.0 | 175.0 | 0.0 | 0.0 | 1.0 | 2.0 | 3.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | 42.0 | 1.0 | 3.0 | 130.0 | 180.0 | 0.0 | 0.0 | 150.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 212 | 57.0 | 0.0 | 4.0 | 120.0 | 354.0 | 0.0 | 0.0 | 163.0 | 1.0 | 0.6 | 1.0 | 0.0 | 3.0 | 1 |
| 213 | 65.0 | 0.0 | 4.0 | 150.0 | 225.0 | 0.0 | 2.0 | 114.0 | 0.0 | 1.0 | 2.0 | 3.0 | 7.0 | 2 |
| 214 | 38.0 | 1.0 | 1.0 | 120.0 | 231.0 | 0.0 | 0.0 | 182.0 | 1.0 | 3.8 | 2.0 | 0.0 | 7.0 | 2 |
| 215 | 47.0 | 1.0 | 4.0 | 112.0 | 204.0 | 0.0 | 0.0 | 143.0 | 0.0 | 0.1 | 1.0 | 0.0 | 3.0 | 1 |
216 rows × 14 columns
small_validation = pd.read_csv(f"heart_test.txt", sep=" ",header=None)
small_validation
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 63.0 | 0.0 | 3.0 | 135.0 | 252.0 | 0.0 | 2.0 | 172.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 1 | 51.0 | 1.0 | 3.0 | 94.0 | 227.0 | 0.0 | 0.0 | 154.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 1 |
| 2 | 54.0 | 1.0 | 3.0 | 120.0 | 258.0 | 0.0 | 2.0 | 147.0 | 0.0 | 0.4 | 2.0 | 0.0 | 7.0 | 1 |
| 3 | 44.0 | 1.0 | 2.0 | 120.0 | 220.0 | 0.0 | 0.0 | 170.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 4 | 54.0 | 1.0 | 4.0 | 110.0 | 239.0 | 0.0 | 0.0 | 126.0 | 1.0 | 2.8 | 2.0 | 1.0 | 7.0 | 2 |
| 5 | 65.0 | 1.0 | 4.0 | 135.0 | 254.0 | 0.0 | 2.0 | 127.0 | 0.0 | 2.8 | 2.0 | 1.0 | 7.0 | 2 |
| 6 | 57.0 | 1.0 | 3.0 | 150.0 | 168.0 | 0.0 | 0.0 | 174.0 | 0.0 | 1.6 | 1.0 | 0.0 | 3.0 | 1 |
| 7 | 63.0 | 1.0 | 4.0 | 130.0 | 330.0 | 1.0 | 2.0 | 132.0 | 1.0 | 1.8 | 1.0 | 3.0 | 7.0 | 2 |
| 8 | 35.0 | 0.0 | 4.0 | 138.0 | 183.0 | 0.0 | 0.0 | 182.0 | 0.0 | 1.4 | 1.0 | 0.0 | 3.0 | 1 |
| 9 | 41.0 | 1.0 | 2.0 | 135.0 | 203.0 | 0.0 | 0.0 | 132.0 | 0.0 | 0.0 | 2.0 | 0.0 | 6.0 | 1 |
| 10 | 62.0 | 0.0 | 3.0 | 130.0 | 263.0 | 0.0 | 0.0 | 97.0 | 0.0 | 1.2 | 2.0 | 1.0 | 7.0 | 2 |
| 11 | 43.0 | 0.0 | 4.0 | 132.0 | 341.0 | 1.0 | 2.0 | 136.0 | 1.0 | 3.0 | 2.0 | 0.0 | 7.0 | 2 |
| 12 | 58.0 | 0.0 | 1.0 | 150.0 | 283.0 | 1.0 | 2.0 | 162.0 | 0.0 | 1.0 | 1.0 | 0.0 | 3.0 | 1 |
| 13 | 52.0 | 1.0 | 1.0 | 118.0 | 186.0 | 0.0 | 2.0 | 190.0 | 0.0 | 0.0 | 2.0 | 0.0 | 6.0 | 1 |
| 14 | 61.0 | 0.0 | 4.0 | 145.0 | 307.0 | 0.0 | 2.0 | 146.0 | 1.0 | 1.0 | 2.0 | 0.0 | 7.0 | 2 |
| 15 | 39.0 | 1.0 | 4.0 | 118.0 | 219.0 | 0.0 | 0.0 | 140.0 | 0.0 | 1.2 | 2.0 | 0.0 | 7.0 | 2 |
| 16 | 45.0 | 1.0 | 4.0 | 115.0 | 260.0 | 0.0 | 2.0 | 185.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 17 | 52.0 | 1.0 | 4.0 | 128.0 | 255.0 | 0.0 | 0.0 | 161.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 2 |
| 18 | 62.0 | 1.0 | 3.0 | 130.0 | 231.0 | 0.0 | 0.0 | 146.0 | 0.0 | 1.8 | 2.0 | 3.0 | 7.0 | 1 |
| 19 | 62.0 | 0.0 | 4.0 | 160.0 | 164.0 | 0.0 | 2.0 | 145.0 | 0.0 | 6.2 | 3.0 | 3.0 | 7.0 | 2 |
| 20 | 53.0 | 0.0 | 4.0 | 138.0 | 234.0 | 0.0 | 2.0 | 160.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 21 | 43.0 | 1.0 | 4.0 | 120.0 | 177.0 | 0.0 | 2.0 | 120.0 | 1.0 | 2.5 | 2.0 | 0.0 | 7.0 | 2 |
| 22 | 47.0 | 1.0 | 3.0 | 138.0 | 257.0 | 0.0 | 2.0 | 156.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 23 | 52.0 | 1.0 | 2.0 | 120.0 | 325.0 | 0.0 | 0.0 | 172.0 | 0.0 | 0.2 | 1.0 | 0.0 | 3.0 | 1 |
| 24 | 68.0 | 1.0 | 3.0 | 180.0 | 274.0 | 1.0 | 2.0 | 150.0 | 1.0 | 1.6 | 2.0 | 0.0 | 7.0 | 2 |
| 25 | 39.0 | 1.0 | 3.0 | 140.0 | 321.0 | 0.0 | 2.0 | 182.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 26 | 53.0 | 0.0 | 4.0 | 130.0 | 264.0 | 0.0 | 2.0 | 143.0 | 0.0 | 0.4 | 2.0 | 0.0 | 3.0 | 1 |
| 27 | 62.0 | 0.0 | 4.0 | 140.0 | 268.0 | 0.0 | 2.0 | 160.0 | 0.0 | 3.6 | 3.0 | 2.0 | 3.0 | 2 |
| 28 | 51.0 | 0.0 | 3.0 | 140.0 | 308.0 | 0.0 | 2.0 | 142.0 | 0.0 | 1.5 | 1.0 | 1.0 | 3.0 | 1 |
| 29 | 60.0 | 1.0 | 4.0 | 130.0 | 253.0 | 0.0 | 0.0 | 144.0 | 1.0 | 1.4 | 1.0 | 1.0 | 7.0 | 2 |
| 30 | 65.0 | 1.0 | 4.0 | 110.0 | 248.0 | 0.0 | 2.0 | 158.0 | 0.0 | 0.6 | 1.0 | 2.0 | 6.0 | 2 |
| 31 | 65.0 | 0.0 | 3.0 | 155.0 | 269.0 | 0.0 | 0.0 | 148.0 | 0.0 | 0.8 | 1.0 | 0.0 | 3.0 | 1 |
| 32 | 60.0 | 1.0 | 3.0 | 140.0 | 185.0 | 0.0 | 2.0 | 155.0 | 0.0 | 3.0 | 2.0 | 0.0 | 3.0 | 2 |
| 33 | 60.0 | 1.0 | 4.0 | 145.0 | 282.0 | 0.0 | 2.0 | 142.0 | 1.0 | 2.8 | 2.0 | 2.0 | 7.0 | 2 |
| 34 | 54.0 | 1.0 | 4.0 | 120.0 | 188.0 | 0.0 | 0.0 | 113.0 | 0.0 | 1.4 | 2.0 | 1.0 | 7.0 | 2 |
| 35 | 44.0 | 1.0 | 2.0 | 130.0 | 219.0 | 0.0 | 2.0 | 188.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 36 | 44.0 | 1.0 | 4.0 | 112.0 | 290.0 | 0.0 | 2.0 | 153.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 | 2 |
| 37 | 51.0 | 1.0 | 3.0 | 110.0 | 175.0 | 0.0 | 0.0 | 123.0 | 0.0 | 0.6 | 1.0 | 0.0 | 3.0 | 1 |
| 38 | 59.0 | 1.0 | 3.0 | 150.0 | 212.0 | 1.0 | 0.0 | 157.0 | 0.0 | 1.6 | 1.0 | 0.0 | 3.0 | 1 |
| 39 | 71.0 | 0.0 | 2.0 | 160.0 | 302.0 | 0.0 | 0.0 | 162.0 | 0.0 | 0.4 | 1.0 | 2.0 | 3.0 | 1 |
| 40 | 61.0 | 1.0 | 3.0 | 150.0 | 243.0 | 1.0 | 0.0 | 137.0 | 1.0 | 1.0 | 2.0 | 0.0 | 3.0 | 1 |
| 41 | 55.0 | 1.0 | 4.0 | 132.0 | 353.0 | 0.0 | 0.0 | 132.0 | 1.0 | 1.2 | 2.0 | 1.0 | 7.0 | 2 |
| 42 | 64.0 | 1.0 | 3.0 | 140.0 | 335.0 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 2 |
| 43 | 43.0 | 1.0 | 4.0 | 150.0 | 247.0 | 0.0 | 0.0 | 171.0 | 0.0 | 1.5 | 1.0 | 0.0 | 3.0 | 1 |
| 44 | 58.0 | 0.0 | 3.0 | 120.0 | 340.0 | 0.0 | 0.0 | 172.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 1 |
| 45 | 60.0 | 1.0 | 4.0 | 130.0 | 206.0 | 0.0 | 2.0 | 132.0 | 1.0 | 2.4 | 2.0 | 2.0 | 7.0 | 2 |
| 46 | 58.0 | 1.0 | 2.0 | 120.0 | 284.0 | 0.0 | 2.0 | 160.0 | 0.0 | 1.8 | 2.0 | 0.0 | 3.0 | 2 |
| 47 | 49.0 | 1.0 | 2.0 | 130.0 | 266.0 | 0.0 | 0.0 | 171.0 | 0.0 | 0.6 | 1.0 | 0.0 | 3.0 | 1 |
| 48 | 48.0 | 1.0 | 2.0 | 110.0 | 229.0 | 0.0 | 0.0 | 168.0 | 0.0 | 1.0 | 3.0 | 0.0 | 7.0 | 2 |
| 49 | 52.0 | 1.0 | 3.0 | 172.0 | 199.0 | 1.0 | 0.0 | 162.0 | 0.0 | 0.5 | 1.0 | 0.0 | 7.0 | 1 |
| 50 | 44.0 | 1.0 | 2.0 | 120.0 | 263.0 | 0.0 | 0.0 | 173.0 | 0.0 | 0.0 | 1.0 | 0.0 | 7.0 | 1 |
| 51 | 56.0 | 0.0 | 2.0 | 140.0 | 294.0 | 0.0 | 2.0 | 153.0 | 0.0 | 1.3 | 2.0 | 0.0 | 3.0 | 1 |
| 52 | 57.0 | 1.0 | 4.0 | 140.0 | 192.0 | 0.0 | 0.0 | 148.0 | 0.0 | 0.4 | 2.0 | 0.0 | 6.0 | 1 |
| 53 | 67.0 | 1.0 | 4.0 | 160.0 | 286.0 | 0.0 | 2.0 | 108.0 | 1.0 | 1.5 | 2.0 | 3.0 | 3.0 | 2 |
small_validation=small_validation.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
Here we will do some data visualizations, statistical analysis, etc.
df.describe()
| age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 | 7560.000000 |
| mean | 54.449074 | 0.666667 | 3.171296 | 130.777778 | 248.967593 | 0.152778 | 1.027778 | 149.148148 | 0.347222 | 1.026389 | 1.597222 | 0.689815 | 4.657407 | 1.444444 |
| std | 9.249095 | 0.471436 | 0.968697 | 18.083740 | 52.246423 | 0.359797 | 0.995038 | 23.757785 | 0.476119 | 1.129028 | 0.616025 | 0.943406 | 1.935003 | 0.496937 |
| min | 29.000000 | 0.000000 | 1.000000 | 94.000000 | 126.000000 | 0.000000 | 0.000000 | 71.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 1.000000 |
| 25% | 47.750000 | 0.000000 | 3.000000 | 120.000000 | 212.750000 | 0.000000 | 0.000000 | 131.750000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 1.000000 |
| 50% | 55.000000 | 1.000000 | 3.000000 | 130.000000 | 243.000000 | 0.000000 | 2.000000 | 153.500000 | 0.000000 | 0.800000 | 2.000000 | 0.000000 | 3.000000 | 1.000000 |
| 75% | 61.000000 | 1.000000 | 4.000000 | 140.000000 | 276.250000 | 0.000000 | 2.000000 | 166.000000 | 1.000000 | 1.800000 | 2.000000 | 1.000000 | 7.000000 | 2.000000 |
| max | 77.000000 | 1.000000 | 4.000000 | 200.000000 | 564.000000 | 1.000000 | 2.000000 | 202.000000 | 1.000000 | 5.600000 | 3.000000 | 3.000000 | 7.000000 | 2.000000 |
sns.heatmap(df.corr(), cmap = 'coolwarm')
<Axes: >
The correlation table highlights that heart problems are mainly linked to different metrics related to the heart (which is logical) but we can add that sex seems to play an important role in the diagnosis of heart disease. This can either highlight that a gender is more affected by heart disease or that the other gender is less likely to be diagnosed with heart disease. Futher analysis cannot be carried out because the sex data is binary and the code associated with male or female is not stated.
sns.pairplot(df,hue='disease')
<seaborn.axisgrid.PairGrid at 0x7b65c00ff160>
In blue the persons without a disease and in orange the persons with a disease.
We pass the variable in binary in order to make a binary classification
df['disease'] = df['disease'].replace({1:0,2:1})
X = df.drop(columns="disease")
y=df["disease"]
y=y.values
y=to_categorical(y)
y.shape
(7560, 2)
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
X_train[:5]
| age | sex | chest_pain | blood_pressure | cholestoral | blood_sugar | electrocardiographic | max_heart_rate | exercise | oldpeak | peak_exer | nb_vessel | defect | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | 59.0 | 1.0 | 2.0 | 140.0 | 221.0 | 0.0 | 0.0 | 164.0 | 1.0 | 0.0 | 1.0 | 0.0 | 3.0 |
| 137 | 65.0 | 1.0 | 4.0 | 120.0 | 177.0 | 0.0 | 0.0 | 140.0 | 0.0 | 0.4 | 1.0 | 0.0 | 7.0 |
| 164 | 69.0 | 1.0 | 3.0 | 140.0 | 254.0 | 0.0 | 2.0 | 146.0 | 0.0 | 2.0 | 2.0 | 3.0 | 7.0 |
| 97 | 57.0 | 1.0 | 3.0 | 150.0 | 126.0 | 1.0 | 0.0 | 173.0 | 0.0 | 0.2 | 1.0 | 1.0 | 7.0 |
| 84 | 63.0 | 0.0 | 4.0 | 108.0 | 269.0 | 0.0 | 0.0 | 169.0 | 1.0 | 1.8 | 2.0 | 2.0 | 3.0 |
Y_train[:5]
array([[1., 0.],
[1., 0.],
[0., 1.],
[1., 0.],
[0., 1.]], dtype=float32)
print("X training set size:", len(X_train))
print("Y training set size:",len(Y_train))
print("X testing set size:",len(X_test))
print("Y testing set size:",len(Y_test))
X training set size: 5292 Y training set size: 5292 X testing set size: 2268 Y testing set size: 2268
We have numerical columns, each of them have different units. In order to reduce the impact of the units we normalize the data.
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
X_train -= mean
X_train /= std
X_test -= mean
X_test /= std
small_df['disease'] = small_df['disease'].replace({1:0,2:1})
X = small_df.drop(columns="disease")
y=small_df["disease"]
y=y.values
y=to_categorical(y)
print("y shape : ",y.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)
#the dataset is really small so we reduce the test set to 20% of the dataset
print("X training set size:", len(X_train))
print("Y training set size:",len(Y_train))
print("X testing set size:",len(X_test))
print("Y testing set size:",len(Y_test))
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
X_train -= mean
X_train /= std
X_test -= mean
X_test /= std
y shape : (216, 2) X training set size: 172 Y training set size: 172 X testing set size: 44 Y testing set size: 44
This is a deep learning assignment so only the deep learning models will be displayed. It was a 3-hour exam so not all the deep learning modeling methods were done.
def smooth_curve(points, factor=0.9):
"""function to plot the learning curves"""
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
def plot_history(history):
"""function to plot the learning curves of the model"""
# plot learning curves
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
####################################
plt.plot(smooth_curve(history.history['accuracy']), label='train')
plt.plot(smooth_curve(history.history['val_accuracy']), label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
#####################
plt.plot(smooth_curve(history.history['loss']), label='train')
plt.plot(smooth_curve(history.history['val_loss']), label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
def creation_model0(X_train,Y_train,X_test,Y_test,earlystop,opti,lrate,l2reg):
"""function to select the optimization method of a model"""
model = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform',kernel_regularizer=keras.regularizers.l2(l2reg)),
#13 neurons because it's the number of columns/inputs
keras.layers.Dense(2, activation='sigmoid')
#2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=earlystop)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
if opti=="adam":
opt=Adam(learning_rate= lrate)
elif opti=="adagrad":
opt=Adagrad(learning_rate= lrate)
elif opti=="SGD":
opt=SGD(learning_rate=0.1, momentum=0.9)
else :
opt= RMSprop(learning_rate= lrate, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0, callbacks=[es, mc])
# plot learning curves
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
####################################
plt.plot(smooth_curve(history.history['accuracy']), label='train')
plt.plot(smooth_curve(history.history['val_accuracy']), label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
#####################
plt.plot(smooth_curve(history.history['loss']), label='train')
plt.plot(smooth_curve(history.history['val_loss']), label='test')
plt.title('lrate='+str(lrate), pad=-50)
plt.legend()
plt.show()
First test:
mod0 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),
#13 neurons because it's the number of columns/inputs
keras.layers.Dense(2, activation='sigmoid')
#2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h0', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod0.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history = mod0.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
saved_model = load_model('best_model.h0')
Epoch 1: val_accuracy improved from -inf to 0.63183, saving model to best_model.h0 Epoch 2: val_accuracy improved from 0.63183 to 0.70503, saving model to best_model.h0 Epoch 3: val_accuracy improved from 0.70503 to 0.75970, saving model to best_model.h0 Epoch 4: val_accuracy improved from 0.75970 to 0.79982, saving model to best_model.h0 Epoch 5: val_accuracy improved from 0.79982 to 0.80511, saving model to best_model.h0 Epoch 6: val_accuracy did not improve from 0.80511 Epoch 7: val_accuracy improved from 0.80511 to 0.82275, saving model to best_model.h0 Epoch 8: val_accuracy did not improve from 0.82275 Epoch 9: val_accuracy improved from 0.82275 to 0.82363, saving model to best_model.h0 Epoch 10: val_accuracy improved from 0.82363 to 0.84259, saving model to best_model.h0 Epoch 11: val_accuracy did not improve from 0.84259 Epoch 12: val_accuracy improved from 0.84259 to 0.84303, saving model to best_model.h0 Epoch 13: val_accuracy improved from 0.84303 to 0.84965, saving model to best_model.h0 Epoch 14: val_accuracy did not improve from 0.84965 Epoch 15: val_accuracy did not improve from 0.84965 Epoch 16: val_accuracy did not improve from 0.84965 Epoch 17: val_accuracy did not improve from 0.84965 Epoch 18: val_accuracy did not improve from 0.84965 Epoch 19: val_accuracy did not improve from 0.84965 Epoch 20: val_accuracy did not improve from 0.84965 Epoch 21: val_accuracy did not improve from 0.84965 Epoch 22: val_accuracy did not improve from 0.84965 Epoch 23: val_accuracy did not improve from 0.84965 Epoch 24: val_accuracy did not improve from 0.84965 Epoch 25: val_accuracy did not improve from 0.84965 Epoch 26: val_accuracy did not improve from 0.84965 Epoch 27: val_accuracy did not improve from 0.84965 Epoch 28: val_accuracy did not improve from 0.84965 Epoch 29: val_accuracy did not improve from 0.84965 Epoch 30: val_accuracy did not improve from 0.84965 Epoch 31: val_accuracy did not improve from 0.84965 Epoch 32: val_accuracy did not improve from 0.84965 Epoch 33: val_accuracy did not improve from 0.84965
mod0.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_4 (Dense) (None, 13) 182
dense_5 (Dense) (None, 2) 28
=================================================================
Total params: 210 (840.00 Byte)
Trainable params: 210 (840.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
plot_history(history)
As you can see the model doesn't overfit (2nd and 3rd graph) but the performance can probably be improved. We will try to add an hidden layer.
mod1 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
keras.layers.Dense(7, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
#mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=35)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod1.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history1 = mod1.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.57231, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 2: val_accuracy improved from 0.57231 to 0.71252, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.71252 to 0.75132, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.75132 to 0.78483, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.78483 to 0.80511, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.80511 to 0.82496, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.82496 to 0.83113, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.83113 Epoch 9: val_accuracy did not improve from 0.83113 Epoch 10: val_accuracy improved from 0.83113 to 0.84171, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.84171 to 0.85009, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.85009 to 0.85582, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.85582 Epoch 14: val_accuracy improved from 0.85582 to 0.85979, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.85979 Epoch 16: val_accuracy improved from 0.85979 to 0.86464, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.86464 Epoch 18: val_accuracy improved from 0.86464 to 0.87522, saving model to best_model.h5 Epoch 19: val_accuracy improved from 0.87522 to 0.88492, saving model to best_model.h5 Epoch 20: val_accuracy improved from 0.88492 to 0.88889, saving model to best_model.h5 Epoch 21: val_accuracy improved from 0.88889 to 0.89242, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.89242 Epoch 23: val_accuracy improved from 0.89242 to 0.89771, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.89771 Epoch 25: val_accuracy did not improve from 0.89771 Epoch 26: val_accuracy improved from 0.89771 to 0.90123, saving model to best_model.h5 Epoch 27: val_accuracy improved from 0.90123 to 0.90653, saving model to best_model.h5 Epoch 28: val_accuracy improved from 0.90653 to 0.91446, saving model to best_model.h5 Epoch 29: val_accuracy improved from 0.91446 to 0.92240, saving model to best_model.h5 Epoch 30: val_accuracy improved from 0.92240 to 0.92593, saving model to best_model.h5 Epoch 31: val_accuracy improved from 0.92593 to 0.92945, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.92945 Epoch 33: val_accuracy did not improve from 0.92945 Epoch 34: val_accuracy did not improve from 0.92945 Epoch 35: val_accuracy improved from 0.92945 to 0.93430, saving model to best_model.h5 Epoch 36: val_accuracy did not improve from 0.93430 Epoch 37: val_accuracy improved from 0.93430 to 0.94444, saving model to best_model.h5 Epoch 38: val_accuracy did not improve from 0.94444 Epoch 39: val_accuracy did not improve from 0.94444 Epoch 40: val_accuracy did not improve from 0.94444 Epoch 41: val_accuracy improved from 0.94444 to 0.95018, saving model to best_model.h5 Epoch 42: val_accuracy improved from 0.95018 to 0.95194, saving model to best_model.h5 Epoch 43: val_accuracy improved from 0.95194 to 0.95767, saving model to best_model.h5 Epoch 44: val_accuracy did not improve from 0.95767 Epoch 45: val_accuracy did not improve from 0.95767 Epoch 46: val_accuracy did not improve from 0.95767 Epoch 47: val_accuracy did not improve from 0.95767 Epoch 48: val_accuracy improved from 0.95767 to 0.96296, saving model to best_model.h5 Epoch 49: val_accuracy improved from 0.96296 to 0.96384, saving model to best_model.h5 Epoch 50: val_accuracy did not improve from 0.96384 Epoch 51: val_accuracy improved from 0.96384 to 0.97840, saving model to best_model.h5 Epoch 52: val_accuracy did not improve from 0.97840 Epoch 53: val_accuracy did not improve from 0.97840 Epoch 54: val_accuracy did not improve from 0.97840 Epoch 55: val_accuracy did not improve from 0.97840 Epoch 56: val_accuracy improved from 0.97840 to 0.98457, saving model to best_model.h5 Epoch 57: val_accuracy did not improve from 0.98457 Epoch 58: val_accuracy did not improve from 0.98457 Epoch 59: val_accuracy did not improve from 0.98457 Epoch 60: val_accuracy did not improve from 0.98457 Epoch 61: val_accuracy did not improve from 0.98457 Epoch 62: val_accuracy did not improve from 0.98457 Epoch 63: val_accuracy did not improve from 0.98457 Epoch 64: val_accuracy did not improve from 0.98457 Epoch 65: val_accuracy improved from 0.98457 to 0.98942, saving model to best_model.h5 Epoch 66: val_accuracy did not improve from 0.98942 Epoch 67: val_accuracy did not improve from 0.98942 Epoch 68: val_accuracy did not improve from 0.98942 Epoch 69: val_accuracy did not improve from 0.98942 Epoch 70: val_accuracy did not improve from 0.98942 Epoch 71: val_accuracy did not improve from 0.98942 Epoch 72: val_accuracy improved from 0.98942 to 0.99471, saving model to best_model.h5 Epoch 73: val_accuracy did not improve from 0.99471 Epoch 74: val_accuracy did not improve from 0.99471 Epoch 75: val_accuracy did not improve from 0.99471 Epoch 76: val_accuracy did not improve from 0.99471 Epoch 77: val_accuracy did not improve from 0.99471 Epoch 78: val_accuracy did not improve from 0.99471 Epoch 79: val_accuracy did not improve from 0.99471 Epoch 80: val_accuracy did not improve from 0.99471 Epoch 81: val_accuracy did not improve from 0.99471 Epoch 82: val_accuracy did not improve from 0.99471 Epoch 83: val_accuracy did not improve from 0.99471 Epoch 84: val_accuracy did not improve from 0.99471 Epoch 85: val_accuracy did not improve from 0.99471 Epoch 86: val_accuracy did not improve from 0.99471 Epoch 87: val_accuracy did not improve from 0.99471 Epoch 88: val_accuracy did not improve from 0.99471 Epoch 89: val_accuracy did not improve from 0.99471 Epoch 90: val_accuracy did not improve from 0.99471 Epoch 91: val_accuracy did not improve from 0.99471 Epoch 92: val_accuracy did not improve from 0.99471 Epoch 93: val_accuracy did not improve from 0.99471 Epoch 94: val_accuracy did not improve from 0.99471 Epoch 95: val_accuracy did not improve from 0.99471 Epoch 96: val_accuracy did not improve from 0.99471 Epoch 97: val_accuracy did not improve from 0.99471 Epoch 98: val_accuracy did not improve from 0.99471 Epoch 99: val_accuracy did not improve from 0.99471 Epoch 100: val_accuracy did not improve from 0.99471 Epoch 101: val_accuracy did not improve from 0.99471 Epoch 102: val_accuracy did not improve from 0.99471 Epoch 103: val_accuracy did not improve from 0.99471 Epoch 104: val_accuracy did not improve from 0.99471 Epoch 105: val_accuracy did not improve from 0.99471 Epoch 106: val_accuracy did not improve from 0.99471 Epoch 107: val_accuracy did not improve from 0.99471
mod1.summary()
Model: "sequential_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_24 (Dense) (None, 13) 182
batch_normalization_12 (Ba (None, 13) 52
tchNormalization)
dense_25 (Dense) (None, 7) 98
batch_normalization_13 (Ba (None, 7) 28
tchNormalization)
dense_26 (Dense) (None, 2) 16
=================================================================
Total params: 376 (1.47 KB)
Trainable params: 336 (1.31 KB)
Non-trainable params: 40 (160.00 Byte)
_________________________________________________________________
plot_history(history1)
By adding one hidden layer we have increase the performances. We will test it with the other number of neurons recommanded.
mod2 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
keras.layers.Dense(8, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
#mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod2.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history2 = mod2.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.73677, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 2: val_accuracy improved from 0.73677 to 0.73765, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.73765 to 0.76455, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.76455 to 0.79189, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.79189 to 0.80291, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.80291 Epoch 7: val_accuracy improved from 0.80291 to 0.80423, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.80423 to 0.82099, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.82099 Epoch 10: val_accuracy improved from 0.82099 to 0.82407, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.82407 to 0.82937, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.82937 to 0.83466, saving model to best_model.h5 Epoch 13: val_accuracy improved from 0.83466 to 0.83862, saving model to best_model.h5 Epoch 14: val_accuracy improved from 0.83862 to 0.84259, saving model to best_model.h5 Epoch 15: val_accuracy improved from 0.84259 to 0.84524, saving model to best_model.h5 Epoch 16: val_accuracy improved from 0.84524 to 0.86243, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.86243 Epoch 18: val_accuracy improved from 0.86243 to 0.86772, saving model to best_model.h5 Epoch 19: val_accuracy improved from 0.86772 to 0.87831, saving model to best_model.h5 Epoch 20: val_accuracy improved from 0.87831 to 0.88757, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.88757 Epoch 22: val_accuracy improved from 0.88757 to 0.89109, saving model to best_model.h5 Epoch 23: val_accuracy improved from 0.89109 to 0.89374, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.89374 Epoch 25: val_accuracy improved from 0.89374 to 0.90873, saving model to best_model.h5 Epoch 26: val_accuracy improved from 0.90873 to 0.92240, saving model to best_model.h5 Epoch 27: val_accuracy did not improve from 0.92240 Epoch 28: val_accuracy did not improve from 0.92240 Epoch 29: val_accuracy did not improve from 0.92240 Epoch 30: val_accuracy did not improve from 0.92240 Epoch 31: val_accuracy did not improve from 0.92240 Epoch 32: val_accuracy improved from 0.92240 to 0.92593, saving model to best_model.h5 Epoch 33: val_accuracy improved from 0.92593 to 0.92945, saving model to best_model.h5 Epoch 34: val_accuracy improved from 0.92945 to 0.93563, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.93563 Epoch 36: val_accuracy did not improve from 0.93563 Epoch 37: val_accuracy did not improve from 0.93563 Epoch 38: val_accuracy did not improve from 0.93563 Epoch 39: val_accuracy improved from 0.93563 to 0.94577, saving model to best_model.h5 Epoch 40: val_accuracy improved from 0.94577 to 0.94797, saving model to best_model.h5 Epoch 41: val_accuracy improved from 0.94797 to 0.95062, saving model to best_model.h5 Epoch 42: val_accuracy did not improve from 0.95062 Epoch 43: val_accuracy did not improve from 0.95062 Epoch 44: val_accuracy did not improve from 0.95062 Epoch 45: val_accuracy did not improve from 0.95062 Epoch 46: val_accuracy improved from 0.95062 to 0.95767, saving model to best_model.h5 Epoch 47: val_accuracy did not improve from 0.95767 Epoch 48: val_accuracy did not improve from 0.95767 Epoch 49: val_accuracy did not improve from 0.95767 Epoch 50: val_accuracy did not improve from 0.95767 Epoch 51: val_accuracy did not improve from 0.95767 Epoch 52: val_accuracy improved from 0.95767 to 0.96384, saving model to best_model.h5 Epoch 53: val_accuracy did not improve from 0.96384 Epoch 54: val_accuracy did not improve from 0.96384 Epoch 55: val_accuracy did not improve from 0.96384 Epoch 56: val_accuracy did not improve from 0.96384 Epoch 57: val_accuracy did not improve from 0.96384 Epoch 58: val_accuracy did not improve from 0.96384 Epoch 59: val_accuracy did not improve from 0.96384 Epoch 60: val_accuracy improved from 0.96384 to 0.96649, saving model to best_model.h5 Epoch 61: val_accuracy did not improve from 0.96649 Epoch 62: val_accuracy did not improve from 0.96649 Epoch 63: val_accuracy did not improve from 0.96649 Epoch 64: val_accuracy did not improve from 0.96649 Epoch 65: val_accuracy did not improve from 0.96649 Epoch 66: val_accuracy did not improve from 0.96649 Epoch 67: val_accuracy improved from 0.96649 to 0.97840, saving model to best_model.h5 Epoch 68: val_accuracy did not improve from 0.97840 Epoch 69: val_accuracy did not improve from 0.97840 Epoch 70: val_accuracy did not improve from 0.97840 Epoch 71: val_accuracy did not improve from 0.97840 Epoch 72: val_accuracy did not improve from 0.97840 Epoch 73: val_accuracy did not improve from 0.97840 Epoch 74: val_accuracy did not improve from 0.97840 Epoch 75: val_accuracy did not improve from 0.97840 Epoch 76: val_accuracy improved from 0.97840 to 0.98192, saving model to best_model.h5 Epoch 77: val_accuracy did not improve from 0.98192 Epoch 78: val_accuracy did not improve from 0.98192 Epoch 79: val_accuracy did not improve from 0.98192 Epoch 80: val_accuracy did not improve from 0.98192 Epoch 81: val_accuracy did not improve from 0.98192 Epoch 82: val_accuracy did not improve from 0.98192 Epoch 83: val_accuracy did not improve from 0.98192 Epoch 84: val_accuracy did not improve from 0.98192 Epoch 85: val_accuracy did not improve from 0.98192 Epoch 86: val_accuracy improved from 0.98192 to 0.98457, saving model to best_model.h5 Epoch 87: val_accuracy did not improve from 0.98457 Epoch 88: val_accuracy did not improve from 0.98457 Epoch 89: val_accuracy did not improve from 0.98457 Epoch 90: val_accuracy did not improve from 0.98457 Epoch 91: val_accuracy did not improve from 0.98457 Epoch 92: val_accuracy did not improve from 0.98457 Epoch 93: val_accuracy did not improve from 0.98457 Epoch 94: val_accuracy did not improve from 0.98457 Epoch 95: val_accuracy did not improve from 0.98457 Epoch 96: val_accuracy did not improve from 0.98457 Epoch 97: val_accuracy did not improve from 0.98457 Epoch 98: val_accuracy did not improve from 0.98457 Epoch 99: val_accuracy did not improve from 0.98457 Epoch 100: val_accuracy did not improve from 0.98457 Epoch 101: val_accuracy did not improve from 0.98457 Epoch 102: val_accuracy did not improve from 0.98457 Epoch 103: val_accuracy did not improve from 0.98457 Epoch 104: val_accuracy did not improve from 0.98457 Epoch 105: val_accuracy did not improve from 0.98457 Epoch 106: val_accuracy improved from 0.98457 to 0.98986, saving model to best_model.h5 Epoch 107: val_accuracy improved from 0.98986 to 0.99515, saving model to best_model.h5 Epoch 108: val_accuracy did not improve from 0.99515 Epoch 109: val_accuracy did not improve from 0.99515 Epoch 110: val_accuracy did not improve from 0.99515 Epoch 111: val_accuracy did not improve from 0.99515 Epoch 112: val_accuracy did not improve from 0.99515 Epoch 113: val_accuracy did not improve from 0.99515 Epoch 114: val_accuracy did not improve from 0.99515 Epoch 115: val_accuracy did not improve from 0.99515 Epoch 116: val_accuracy did not improve from 0.99515 Epoch 117: val_accuracy did not improve from 0.99515 Epoch 118: val_accuracy did not improve from 0.99515 Epoch 119: val_accuracy did not improve from 0.99515 Epoch 120: val_accuracy did not improve from 0.99515 Epoch 121: val_accuracy did not improve from 0.99515 Epoch 122: val_accuracy did not improve from 0.99515 Epoch 123: val_accuracy did not improve from 0.99515 Epoch 124: val_accuracy did not improve from 0.99515 Epoch 125: val_accuracy did not improve from 0.99515 Epoch 126: val_accuracy did not improve from 0.99515 Epoch 127: val_accuracy did not improve from 0.99515
mod2.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_27 (Dense) (None, 13) 182
batch_normalization_14 (Ba (None, 13) 52
tchNormalization)
dense_28 (Dense) (None, 8) 112
batch_normalization_15 (Ba (None, 8) 32
tchNormalization)
dense_29 (Dense) (None, 2) 18
=================================================================
Total params: 396 (1.55 KB)
Trainable params: 354 (1.38 KB)
Non-trainable params: 42 (168.00 Byte)
_________________________________________________________________
plot_history(history2)
By adding an additional neuron we further increased the performance of our models without overfitting.
The last models is excellent, so I select it as the final model for the prediction of the validation set.
X = validation.drop(columns="disease")
y=validation["disease"]
y.shape
(1080,)
X-= mean
X /= std
predictions = mod2.predict(X)
34/34 [==============================] - 0s 2ms/step
predictions[:5]
array([[8.4876359e-01, 1.2433447e-01],
[3.5142712e-02, 9.6764618e-01],
[1.7565981e-04, 9.9975723e-01],
[9.9389571e-01, 4.6091015e-03],
[9.8444611e-01, 1.3892172e-02]], dtype=float32)
result =[]
for i in range(len(predictions)):
if predictions[i][0]>predictions[i][1]:
result.append(1)#the real class
else :
result.append(2)#the real class
score = accuracy_score(validation["disease"], result)
print("accuracy : "+str(score))
accuracy : 0.9953703703703703
#writing results for exam evaluation
r= open("results_big_dataset.txt", "w+")
r.write(str(result))
r.close()
mod0 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),
#13 neurons because it's the number of columns/inputs
keras.layers.Dense(2, activation='sigmoid')
#2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h0', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod0.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history = mod0.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h0 Epoch 2: val_accuracy did not improve from 0.43182 Epoch 3: val_accuracy did not improve from 0.43182 Epoch 4: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h0 Epoch 5: val_accuracy did not improve from 0.45455 Epoch 6: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h0 Epoch 7: val_accuracy did not improve from 0.47727 Epoch 8: val_accuracy did not improve from 0.47727 Epoch 9: val_accuracy did not improve from 0.47727 Epoch 10: val_accuracy did not improve from 0.47727 Epoch 11: val_accuracy did not improve from 0.47727 Epoch 12: val_accuracy did not improve from 0.47727 Epoch 13: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h0 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy did not improve from 0.50000 Epoch 16: val_accuracy did not improve from 0.50000 Epoch 17: val_accuracy did not improve from 0.50000 Epoch 18: val_accuracy did not improve from 0.50000 Epoch 19: val_accuracy did not improve from 0.50000 Epoch 20: val_accuracy did not improve from 0.50000 Epoch 21: val_accuracy did not improve from 0.50000 Epoch 22: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h0 Epoch 23: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h0 Epoch 24: val_accuracy did not improve from 0.54545 Epoch 25: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h0 Epoch 26: val_accuracy did not improve from 0.56818 Epoch 27: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h0 Epoch 28: val_accuracy did not improve from 0.59091 Epoch 29: val_accuracy did not improve from 0.59091 Epoch 30: val_accuracy did not improve from 0.59091 Epoch 31: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h0 Epoch 32: val_accuracy did not improve from 0.61364 Epoch 33: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h0 Epoch 34: val_accuracy did not improve from 0.63636 Epoch 35: val_accuracy did not improve from 0.63636 Epoch 36: val_accuracy did not improve from 0.63636 Epoch 37: val_accuracy did not improve from 0.63636 Epoch 38: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h0 Epoch 39: val_accuracy did not improve from 0.65909 Epoch 40: val_accuracy did not improve from 0.65909 Epoch 41: val_accuracy did not improve from 0.65909 Epoch 42: val_accuracy did not improve from 0.65909 Epoch 43: val_accuracy did not improve from 0.65909 Epoch 44: val_accuracy did not improve from 0.65909 Epoch 45: val_accuracy did not improve from 0.65909 Epoch 46: val_accuracy did not improve from 0.65909 Epoch 47: val_accuracy did not improve from 0.65909 Epoch 48: val_accuracy did not improve from 0.65909 Epoch 49: val_accuracy did not improve from 0.65909 Epoch 50: val_accuracy did not improve from 0.65909 Epoch 51: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h0 Epoch 52: val_accuracy did not improve from 0.68182 Epoch 53: val_accuracy did not improve from 0.68182 Epoch 54: val_accuracy did not improve from 0.68182 Epoch 55: val_accuracy did not improve from 0.68182 Epoch 56: val_accuracy did not improve from 0.68182 Epoch 57: val_accuracy did not improve from 0.68182 Epoch 58: val_accuracy did not improve from 0.68182 Epoch 59: val_accuracy did not improve from 0.68182 Epoch 60: val_accuracy did not improve from 0.68182 Epoch 61: val_accuracy did not improve from 0.68182 Epoch 62: val_accuracy did not improve from 0.68182 Epoch 63: val_accuracy did not improve from 0.68182 Epoch 64: val_accuracy did not improve from 0.68182 Epoch 65: val_accuracy did not improve from 0.68182 Epoch 66: val_accuracy did not improve from 0.68182 Epoch 67: val_accuracy did not improve from 0.68182 Epoch 68: val_accuracy did not improve from 0.68182 Epoch 69: val_accuracy did not improve from 0.68182 Epoch 70: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h0 Epoch 71: val_accuracy did not improve from 0.70455 Epoch 72: val_accuracy did not improve from 0.70455 Epoch 73: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h0 Epoch 74: val_accuracy did not improve from 0.72727 Epoch 75: val_accuracy did not improve from 0.72727 Epoch 76: val_accuracy did not improve from 0.72727 Epoch 77: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h0 Epoch 78: val_accuracy did not improve from 0.77273 Epoch 79: val_accuracy did not improve from 0.77273 Epoch 80: val_accuracy did not improve from 0.77273 Epoch 81: val_accuracy did not improve from 0.77273 Epoch 82: val_accuracy did not improve from 0.77273 Epoch 83: val_accuracy did not improve from 0.77273 Epoch 84: val_accuracy did not improve from 0.77273 Epoch 85: val_accuracy did not improve from 0.77273 Epoch 86: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h0 Epoch 87: val_accuracy did not improve from 0.79545 Epoch 88: val_accuracy did not improve from 0.79545 Epoch 89: val_accuracy did not improve from 0.79545 Epoch 90: val_accuracy did not improve from 0.79545 Epoch 91: val_accuracy did not improve from 0.79545 Epoch 92: val_accuracy did not improve from 0.79545 Epoch 93: val_accuracy did not improve from 0.79545 Epoch 94: val_accuracy did not improve from 0.79545 Epoch 95: val_accuracy did not improve from 0.79545 Epoch 96: val_accuracy did not improve from 0.79545 Epoch 97: val_accuracy did not improve from 0.79545 Epoch 98: val_accuracy did not improve from 0.79545 Epoch 99: val_accuracy did not improve from 0.79545 Epoch 100: val_accuracy did not improve from 0.79545 Epoch 101: val_accuracy did not improve from 0.79545 Epoch 102: val_accuracy did not improve from 0.79545 Epoch 103: val_accuracy did not improve from 0.79545 Epoch 104: val_accuracy did not improve from 0.79545 Epoch 105: val_accuracy did not improve from 0.79545 Epoch 106: val_accuracy did not improve from 0.79545
mod0.summary()
Model: "sequential_12"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_32 (Dense) (None, 13) 182
dense_33 (Dense) (None, 2) 28
=================================================================
Total params: 210 (840.00 Byte)
Trainable params: 210 (840.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
plot_history(history)
Performance is good for a first try with so little data. It overfit more than with the large dataset, but it's normal due to the lack of data. They are less good than with the large dataset because there is less data because it is more difficult to correctly generalize the information when there is a lack of data.
We will try to add one hidden layer.
mod1 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
keras.layers.Dense(7, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
#mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod1.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history1 = mod1.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.47727 Epoch 3: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.56818 Epoch 9: val_accuracy did not improve from 0.56818 Epoch 10: val_accuracy did not improve from 0.56818 Epoch 11: val_accuracy did not improve from 0.56818 Epoch 12: val_accuracy did not improve from 0.56818 Epoch 13: val_accuracy did not improve from 0.56818 Epoch 14: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 15: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.61364 Epoch 17: val_accuracy did not improve from 0.61364 Epoch 18: val_accuracy did not improve from 0.61364 Epoch 19: val_accuracy did not improve from 0.61364 Epoch 20: val_accuracy did not improve from 0.61364 Epoch 21: val_accuracy did not improve from 0.61364 Epoch 22: val_accuracy did not improve from 0.61364 Epoch 23: val_accuracy did not improve from 0.61364 Epoch 24: val_accuracy did not improve from 0.61364 Epoch 25: val_accuracy did not improve from 0.61364 Epoch 26: val_accuracy did not improve from 0.61364 Epoch 27: val_accuracy did not improve from 0.61364 Epoch 28: val_accuracy did not improve from 0.61364 Epoch 29: val_accuracy did not improve from 0.61364 Epoch 30: val_accuracy did not improve from 0.61364 Epoch 31: val_accuracy did not improve from 0.61364 Epoch 32: val_accuracy did not improve from 0.61364 Epoch 33: val_accuracy did not improve from 0.61364 Epoch 34: val_accuracy did not improve from 0.61364 Epoch 35: val_accuracy did not improve from 0.61364
mod1.summary()
Model: "sequential_14"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_37 (Dense) (None, 13) 182
batch_normalization_18 (Ba (None, 13) 52
tchNormalization)
dense_38 (Dense) (None, 7) 98
batch_normalization_19 (Ba (None, 7) 28
tchNormalization)
dense_39 (Dense) (None, 2) 16
=================================================================
Total params: 376 (1.47 KB)
Trainable params: 336 (1.31 KB)
Non-trainable params: 40 (160.00 Byte)
_________________________________________________________________
plot_history(history1)
mod2 = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
keras.layers.Dense(8, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
#mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod2.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history2 = mod2.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.70455 Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy did not improve from 0.70455 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy did not improve from 0.70455 Epoch 10: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy did not improve from 0.72727 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy did not improve from 0.72727 Epoch 16: val_accuracy did not improve from 0.72727 Epoch 17: val_accuracy did not improve from 0.72727 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy did not improve from 0.72727 Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 33: val_accuracy did not improve from 0.79545 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 37: val_accuracy did not improve from 0.81818 Epoch 38: val_accuracy did not improve from 0.81818 Epoch 39: val_accuracy did not improve from 0.81818 Epoch 40: val_accuracy did not improve from 0.81818 Epoch 41: val_accuracy did not improve from 0.81818 Epoch 42: val_accuracy did not improve from 0.81818 Epoch 43: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 44: val_accuracy did not improve from 0.84091 Epoch 45: val_accuracy did not improve from 0.84091 Epoch 46: val_accuracy did not improve from 0.84091 Epoch 47: val_accuracy did not improve from 0.84091 Epoch 48: val_accuracy did not improve from 0.84091 Epoch 49: val_accuracy did not improve from 0.84091 Epoch 50: val_accuracy did not improve from 0.84091 Epoch 51: val_accuracy did not improve from 0.84091 Epoch 52: val_accuracy did not improve from 0.84091 Epoch 53: val_accuracy did not improve from 0.84091 Epoch 54: val_accuracy did not improve from 0.84091 Epoch 55: val_accuracy did not improve from 0.84091 Epoch 56: val_accuracy did not improve from 0.84091 Epoch 57: val_accuracy did not improve from 0.84091 Epoch 58: val_accuracy did not improve from 0.84091 Epoch 59: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 60: val_accuracy did not improve from 0.86364 Epoch 61: val_accuracy did not improve from 0.86364 Epoch 62: val_accuracy did not improve from 0.86364 Epoch 63: val_accuracy did not improve from 0.86364 Epoch 64: val_accuracy did not improve from 0.86364 Epoch 65: val_accuracy did not improve from 0.86364 Epoch 66: val_accuracy did not improve from 0.86364 Epoch 67: val_accuracy did not improve from 0.86364 Epoch 68: val_accuracy did not improve from 0.86364 Epoch 69: val_accuracy did not improve from 0.86364 Epoch 70: val_accuracy did not improve from 0.86364 Epoch 71: val_accuracy did not improve from 0.86364 Epoch 72: val_accuracy did not improve from 0.86364 Epoch 73: val_accuracy did not improve from 0.86364 Epoch 74: val_accuracy did not improve from 0.86364 Epoch 75: val_accuracy did not improve from 0.86364 Epoch 76: val_accuracy did not improve from 0.86364 Epoch 77: val_accuracy did not improve from 0.86364 Epoch 78: val_accuracy did not improve from 0.86364 Epoch 79: val_accuracy did not improve from 0.86364
mod2.summary()
Model: "sequential_15"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_40 (Dense) (None, 13) 182
batch_normalization_20 (Ba (None, 13) 52
tchNormalization)
dense_41 (Dense) (None, 8) 112
batch_normalization_21 (Ba (None, 8) 32
tchNormalization)
dense_42 (Dense) (None, 2) 18
=================================================================
Total params: 396 (1.55 KB)
Trainable params: 354 (1.38 KB)
Non-trainable params: 42 (168.00 Byte)
_________________________________________________________________
plot_history(history2)
As you can see the two models with hidden layers are overfitting. This is a result we could expect given that we did not have much data.
To try to have a good prediction we will optimize the first model. In the following part, we will test the different optimization methods.
learning_rates = [1E-0, 1E-1, 1E-2, 1E-3,0.005,0.0025, 1E-4, 1E-5, 1E-6, 1E-7]
l2reg = [1,0.1,0.01,0.001,0.0001,0.0001]
classement =[]
ind=3
for k in range(len(l2reg)):
for j,opti in enumerate(["adam","adagrad","SGD"]):
for i in range(len(learning_rates)):
print("\n\n#######################################################\n\n")
print(f"the model mod{ind} use a learning rate = {i}, l2 regularization = {k} and the optimizer = {opti} :")
try :
globals()[f"mod{ind}"] = creation_model0(X_train,Y_train,X_test,Y_test,20,opti,learning_rates[i],l2reg[k])
classement.append([globals()[f"mod{ind}"].evaluate(X_test, Y_test, verbose=2)[1],f"mod{ind}"])
except Exception as e:
print(e)
ind+=1
####################################################### the model mod3 use a learning rate = 0, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy did not improve from 0.54545 Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy did not improve from 0.54545 Epoch 8: val_accuracy did not improve from 0.54545 Epoch 9: val_accuracy did not improve from 0.54545 Epoch 10: val_accuracy did not improve from 0.54545 Epoch 11: val_accuracy did not improve from 0.54545 Epoch 12: val_accuracy did not improve from 0.54545 Epoch 13: val_accuracy did not improve from 0.54545 Epoch 14: val_accuracy did not improve from 0.54545 Epoch 15: val_accuracy did not improve from 0.54545 Epoch 16: val_accuracy did not improve from 0.54545 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy did not improve from 0.54545 Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 0.6909 - accuracy: 0.5455 - 24ms/epoch - 12ms/step ####################################################### the model mod4 use a learning rate = 1, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy improved from 0.54545 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5070 - accuracy: 0.8636 - 88ms/epoch - 44ms/step ####################################################### the model mod5 use a learning rate = 2, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.56818 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.68182 to 0.75000, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.77273 Epoch 7: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.79545 Epoch 9: val_accuracy did not improve from 0.79545 Epoch 10: val_accuracy did not improve from 0.79545 Epoch 11: val_accuracy did not improve from 0.79545 Epoch 12: val_accuracy did not improve from 0.79545 Epoch 13: val_accuracy did not improve from 0.79545 Epoch 14: val_accuracy did not improve from 0.79545 Epoch 15: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091 Epoch 22: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909 Epoch 43: val_accuracy did not improve from 0.90909 Epoch 44: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4113 - accuracy: 0.9091 - 23ms/epoch - 12ms/step ####################################################### the model mod6 use a learning rate = 3, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.25000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.25000 Epoch 3: val_accuracy did not improve from 0.25000 Epoch 4: val_accuracy did not improve from 0.25000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.25000 Epoch 6: val_accuracy did not improve from 0.25000 Epoch 7: val_accuracy improved from 0.25000 to 0.27273, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.27273 Epoch 9: val_accuracy improved from 0.27273 to 0.29545, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.29545 Epoch 11: val_accuracy improved from 0.29545 to 0.31818, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.31818 to 0.34091, saving model to best_model.h5 Epoch 13: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5 Epoch 14: val_accuracy improved from 0.36364 to 0.40909, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.40909 Epoch 16: val_accuracy did not improve from 0.40909 Epoch 17: val_accuracy did not improve from 0.40909 Epoch 18: val_accuracy did not improve from 0.40909 Epoch 19: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 20: val_accuracy did not improve from 0.43182 Epoch 21: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 22: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.47727 Epoch 24: val_accuracy did not improve from 0.47727 Epoch 25: val_accuracy did not improve from 0.47727 Epoch 26: val_accuracy did not improve from 0.47727 Epoch 27: val_accuracy did not improve from 0.47727 Epoch 28: val_accuracy did not improve from 0.47727 Epoch 29: val_accuracy did not improve from 0.47727 Epoch 30: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5 Epoch 31: val_accuracy did not improve from 0.52273 Epoch 32: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5 Epoch 33: val_accuracy did not improve from 0.59091 Epoch 34: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5 Epoch 35: val_accuracy improved from 0.65909 to 0.75000, saving model to best_model.h5 Epoch 36: val_accuracy did not improve from 0.75000 Epoch 37: val_accuracy did not improve from 0.75000 Epoch 38: val_accuracy did not improve from 0.75000 Epoch 39: val_accuracy did not improve from 0.75000 Epoch 40: val_accuracy did not improve from 0.75000 Epoch 41: val_accuracy did not improve from 0.75000 Epoch 42: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 43: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 44: val_accuracy did not improve from 0.79545 Epoch 45: val_accuracy did not improve from 0.79545 Epoch 46: val_accuracy did not improve from 0.79545 Epoch 47: val_accuracy did not improve from 0.79545 Epoch 48: val_accuracy did not improve from 0.79545 Epoch 49: val_accuracy did not improve from 0.79545 Epoch 50: val_accuracy did not improve from 0.79545 Epoch 51: val_accuracy did not improve from 0.79545 Epoch 52: val_accuracy did not improve from 0.79545 Epoch 53: val_accuracy did not improve from 0.79545 Epoch 54: val_accuracy did not improve from 0.79545 Epoch 55: val_accuracy did not improve from 0.79545 Epoch 56: val_accuracy did not improve from 0.79545 Epoch 57: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 58: val_accuracy did not improve from 0.81818 Epoch 59: val_accuracy did not improve from 0.81818 Epoch 60: val_accuracy did not improve from 0.81818 Epoch 61: val_accuracy did not improve from 0.81818 Epoch 62: val_accuracy did not improve from 0.81818 Epoch 63: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 64: val_accuracy did not improve from 0.84091 Epoch 65: val_accuracy did not improve from 0.84091 Epoch 66: val_accuracy did not improve from 0.84091 Epoch 67: val_accuracy did not improve from 0.84091 Epoch 68: val_accuracy did not improve from 0.84091 Epoch 69: val_accuracy did not improve from 0.84091 Epoch 70: val_accuracy did not improve from 0.84091 Epoch 71: val_accuracy did not improve from 0.84091 Epoch 72: val_accuracy did not improve from 0.84091 Epoch 73: val_accuracy did not improve from 0.84091 Epoch 74: val_accuracy did not improve from 0.84091 Epoch 75: val_accuracy did not improve from 0.84091 Epoch 76: val_accuracy did not improve from 0.84091 Epoch 77: val_accuracy did not improve from 0.84091 Epoch 78: val_accuracy did not improve from 0.84091 Epoch 79: val_accuracy did not improve from 0.84091 Epoch 80: val_accuracy did not improve from 0.84091 Epoch 81: val_accuracy did not improve from 0.84091 Epoch 82: val_accuracy did not improve from 0.84091 Epoch 83: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 2.8465 - accuracy: 0.7955 - 25ms/epoch - 13ms/step ####################################################### the model mod7 use a learning rate = 4, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.61364 Epoch 3: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182 Epoch 6: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.72727 Epoch 8: val_accuracy did not improve from 0.72727 Epoch 9: val_accuracy did not improve from 0.72727 Epoch 10: val_accuracy did not improve from 0.72727 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy did not improve from 0.75000 Epoch 15: val_accuracy did not improve from 0.75000 Epoch 16: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.77273 Epoch 18: val_accuracy did not improve from 0.77273 Epoch 19: val_accuracy did not improve from 0.77273 Epoch 20: val_accuracy did not improve from 0.77273 Epoch 21: val_accuracy did not improve from 0.77273 Epoch 22: val_accuracy did not improve from 0.77273 Epoch 23: val_accuracy did not improve from 0.77273 Epoch 24: val_accuracy did not improve from 0.77273 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy did not improve from 0.77273 Epoch 34: val_accuracy did not improve from 0.77273 Epoch 35: val_accuracy did not improve from 0.77273 Epoch 36: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5710 - accuracy: 0.7727 - 38ms/epoch - 19ms/step ####################################################### the model mod8 use a learning rate = 5, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.40909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.40909 Epoch 3: val_accuracy did not improve from 0.40909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.40909 Epoch 5: val_accuracy did not improve from 0.40909 Epoch 6: val_accuracy improved from 0.40909 to 0.45455, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.45455 Epoch 8: val_accuracy did not improve from 0.45455 Epoch 9: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.61364 Epoch 14: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 15: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 16: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.70455 Epoch 18: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 19: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 20: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 21: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818 Epoch 31: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 32: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 33: val_accuracy did not improve from 0.86364 Epoch 34: val_accuracy did not improve from 0.86364 Epoch 35: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 36: val_accuracy did not improve from 0.88636 Epoch 37: val_accuracy did not improve from 0.88636 Epoch 38: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.93182 Epoch 40: val_accuracy did not improve from 0.93182 Epoch 41: val_accuracy did not improve from 0.93182 Epoch 42: val_accuracy did not improve from 0.93182 Epoch 43: val_accuracy did not improve from 0.93182 Epoch 44: val_accuracy did not improve from 0.93182 Epoch 45: val_accuracy did not improve from 0.93182 Epoch 46: val_accuracy did not improve from 0.93182 Epoch 47: val_accuracy did not improve from 0.93182 Epoch 48: val_accuracy did not improve from 0.93182 Epoch 49: val_accuracy did not improve from 0.93182 Epoch 50: val_accuracy did not improve from 0.93182 Epoch 51: val_accuracy did not improve from 0.93182 Epoch 52: val_accuracy did not improve from 0.93182 Epoch 53: val_accuracy did not improve from 0.93182 Epoch 54: val_accuracy did not improve from 0.93182 Epoch 55: val_accuracy did not improve from 0.93182 Epoch 56: val_accuracy did not improve from 0.93182 Epoch 57: val_accuracy did not improve from 0.93182 Epoch 58: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.7310 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod9 use a learning rate = 6, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455 Epoch 4: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.72727 Epoch 6: val_accuracy did not improve from 0.72727 Epoch 7: val_accuracy did not improve from 0.72727 Epoch 8: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy did not improve from 0.75000 Epoch 15: val_accuracy did not improve from 0.75000 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy did not improve from 0.75000 Epoch 25: val_accuracy did not improve from 0.75000 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy did not improve from 0.75000 Epoch 28: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 25.4139 - accuracy: 0.7273 - 24ms/epoch - 12ms/step ####################################################### the model mod10 use a learning rate = 7, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455 Epoch 4: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy did not improve from 0.70455 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy did not improve from 0.70455 Epoch 10: val_accuracy did not improve from 0.70455 Epoch 11: val_accuracy did not improve from 0.70455 Epoch 12: val_accuracy did not improve from 0.70455 Epoch 13: val_accuracy did not improve from 0.70455 Epoch 14: val_accuracy did not improve from 0.70455 Epoch 15: val_accuracy did not improve from 0.70455 Epoch 16: val_accuracy did not improve from 0.70455 Epoch 17: val_accuracy did not improve from 0.70455 Epoch 18: val_accuracy did not improve from 0.70455 Epoch 19: val_accuracy did not improve from 0.70455 Epoch 20: val_accuracy did not improve from 0.70455 Epoch 21: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 27.0857 - accuracy: 0.7045 - 26ms/epoch - 13ms/step ####################################################### the model mod11 use a learning rate = 8, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.68182 Epoch 3: val_accuracy did not improve from 0.68182 Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182 Epoch 6: val_accuracy did not improve from 0.68182 Epoch 7: val_accuracy did not improve from 0.68182 Epoch 8: val_accuracy did not improve from 0.68182 Epoch 9: val_accuracy did not improve from 0.68182 Epoch 10: val_accuracy did not improve from 0.68182 Epoch 11: val_accuracy did not improve from 0.68182 Epoch 12: val_accuracy did not improve from 0.68182 Epoch 13: val_accuracy did not improve from 0.68182 Epoch 14: val_accuracy did not improve from 0.68182 Epoch 15: val_accuracy did not improve from 0.68182 Epoch 16: val_accuracy did not improve from 0.68182 Epoch 17: val_accuracy did not improve from 0.68182 Epoch 18: val_accuracy did not improve from 0.68182 Epoch 19: val_accuracy did not improve from 0.68182 Epoch 20: val_accuracy did not improve from 0.68182 Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 29.3636 - accuracy: 0.6818 - 32ms/epoch - 16ms/step ####################################################### the model mod12 use a learning rate = 9, l2 regularization = 0 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.47727 Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727 Epoch 5: val_accuracy did not improve from 0.47727 Epoch 6: val_accuracy did not improve from 0.47727 Epoch 7: val_accuracy did not improve from 0.47727 Epoch 8: val_accuracy did not improve from 0.47727 Epoch 9: val_accuracy did not improve from 0.47727 Epoch 10: val_accuracy did not improve from 0.47727 Epoch 11: val_accuracy did not improve from 0.47727 Epoch 12: val_accuracy did not improve from 0.47727 Epoch 13: val_accuracy did not improve from 0.47727 Epoch 14: val_accuracy did not improve from 0.47727 Epoch 15: val_accuracy did not improve from 0.47727 Epoch 16: val_accuracy did not improve from 0.47727 Epoch 17: val_accuracy did not improve from 0.47727 Epoch 18: val_accuracy did not improve from 0.47727 Epoch 19: val_accuracy did not improve from 0.47727 Epoch 20: val_accuracy did not improve from 0.47727 Epoch 21: val_accuracy did not improve from 0.47727
2/2 - 0s - loss: 26.1684 - accuracy: 0.4773 - 26ms/epoch - 13ms/step ####################################################### the model mod13 use a learning rate = 0, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.84091 Epoch 3: val_accuracy did not improve from 0.84091 Epoch 4: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy did not improve from 0.84091 Epoch 17: val_accuracy did not improve from 0.84091 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.9606 - accuracy: 0.5000 - 25ms/epoch - 12ms/step ####################################################### the model mod14 use a learning rate = 1, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.56818 Epoch 4: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.61364 to 0.72727, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.72727 Epoch 8: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.77273 Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 13: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4648 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod15 use a learning rate = 2, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.59091 Epoch 4: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.61364 Epoch 7: val_accuracy did not improve from 0.61364 Epoch 8: val_accuracy did not improve from 0.61364 Epoch 9: val_accuracy did not improve from 0.61364 Epoch 10: val_accuracy did not improve from 0.61364 Epoch 11: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.63636 Epoch 13: val_accuracy did not improve from 0.63636 Epoch 14: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.68182 Epoch 16: val_accuracy did not improve from 0.68182 Epoch 17: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy did not improve from 0.72727 Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy did not improve from 0.75000 Epoch 25: val_accuracy did not improve from 0.75000 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy did not improve from 0.75000 Epoch 28: val_accuracy did not improve from 0.75000 Epoch 29: val_accuracy did not improve from 0.75000 Epoch 30: val_accuracy did not improve from 0.75000 Epoch 31: val_accuracy did not improve from 0.75000 Epoch 32: val_accuracy did not improve from 0.75000 Epoch 33: val_accuracy did not improve from 0.75000 Epoch 34: val_accuracy did not improve from 0.75000 Epoch 35: val_accuracy did not improve from 0.75000 Epoch 36: val_accuracy did not improve from 0.75000 Epoch 37: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 38: val_accuracy did not improve from 0.77273 Epoch 39: val_accuracy did not improve from 0.77273 Epoch 40: val_accuracy did not improve from 0.77273 Epoch 41: val_accuracy did not improve from 0.77273 Epoch 42: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5 Epoch 43: val_accuracy did not improve from 0.81818 Epoch 44: val_accuracy did not improve from 0.81818 Epoch 45: val_accuracy did not improve from 0.81818 Epoch 46: val_accuracy did not improve from 0.81818 Epoch 47: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 48: val_accuracy did not improve from 0.84091 Epoch 49: val_accuracy did not improve from 0.84091 Epoch 50: val_accuracy did not improve from 0.84091 Epoch 51: val_accuracy did not improve from 0.84091 Epoch 52: val_accuracy did not improve from 0.84091 Epoch 53: val_accuracy did not improve from 0.84091 Epoch 54: val_accuracy did not improve from 0.84091 Epoch 55: val_accuracy did not improve from 0.84091 Epoch 56: val_accuracy did not improve from 0.84091 Epoch 57: val_accuracy did not improve from 0.84091 Epoch 58: val_accuracy did not improve from 0.84091 Epoch 59: val_accuracy did not improve from 0.84091 Epoch 60: val_accuracy did not improve from 0.84091 Epoch 61: val_accuracy did not improve from 0.84091 Epoch 62: val_accuracy did not improve from 0.84091 Epoch 63: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 64: val_accuracy did not improve from 0.86364 Epoch 65: val_accuracy did not improve from 0.86364 Epoch 66: val_accuracy did not improve from 0.86364 Epoch 67: val_accuracy did not improve from 0.86364 Epoch 68: val_accuracy did not improve from 0.86364 Epoch 69: val_accuracy did not improve from 0.86364 Epoch 70: val_accuracy did not improve from 0.86364 Epoch 71: val_accuracy did not improve from 0.86364 Epoch 72: val_accuracy did not improve from 0.86364 Epoch 73: val_accuracy did not improve from 0.86364 Epoch 74: val_accuracy did not improve from 0.86364 Epoch 75: val_accuracy did not improve from 0.86364 Epoch 76: val_accuracy did not improve from 0.86364 Epoch 77: val_accuracy did not improve from 0.86364 Epoch 78: val_accuracy did not improve from 0.86364 Epoch 79: val_accuracy did not improve from 0.86364 Epoch 80: val_accuracy did not improve from 0.86364 Epoch 81: val_accuracy did not improve from 0.86364 Epoch 82: val_accuracy did not improve from 0.86364 Epoch 83: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 3.4218 - accuracy: 0.8636 - 27ms/epoch - 14ms/step ####################################################### the model mod16 use a learning rate = 3, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.68182 Epoch 3: val_accuracy did not improve from 0.68182 Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182 Epoch 6: val_accuracy did not improve from 0.68182 Epoch 7: val_accuracy did not improve from 0.68182 Epoch 8: val_accuracy did not improve from 0.68182 Epoch 9: val_accuracy did not improve from 0.68182 Epoch 10: val_accuracy did not improve from 0.68182 Epoch 11: val_accuracy did not improve from 0.68182 Epoch 12: val_accuracy did not improve from 0.68182 Epoch 13: val_accuracy did not improve from 0.68182 Epoch 14: val_accuracy did not improve from 0.68182 Epoch 15: val_accuracy did not improve from 0.68182 Epoch 16: val_accuracy did not improve from 0.68182 Epoch 17: val_accuracy did not improve from 0.68182 Epoch 18: val_accuracy did not improve from 0.68182 Epoch 19: val_accuracy did not improve from 0.68182 Epoch 20: val_accuracy did not improve from 0.68182 Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 25.0937 - accuracy: 0.6818 - 35ms/epoch - 18ms/step ####################################################### the model mod17 use a learning rate = 4, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.77273 Epoch 10: val_accuracy did not improve from 0.77273 Epoch 11: val_accuracy did not improve from 0.77273 Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.79545 Epoch 14: val_accuracy did not improve from 0.79545 Epoch 15: val_accuracy did not improve from 0.79545 Epoch 16: val_accuracy did not improve from 0.79545 Epoch 17: val_accuracy did not improve from 0.79545 Epoch 18: val_accuracy did not improve from 0.79545 Epoch 19: val_accuracy did not improve from 0.79545 Epoch 20: val_accuracy did not improve from 0.79545 Epoch 21: val_accuracy did not improve from 0.79545 Epoch 22: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818 Epoch 31: val_accuracy did not improve from 0.81818 Epoch 32: val_accuracy did not improve from 0.81818 Epoch 33: val_accuracy did not improve from 0.81818 Epoch 34: val_accuracy did not improve from 0.81818 Epoch 35: val_accuracy did not improve from 0.81818 Epoch 36: val_accuracy did not improve from 0.81818 Epoch 37: val_accuracy did not improve from 0.81818 Epoch 38: val_accuracy did not improve from 0.81818 Epoch 39: val_accuracy did not improve from 0.81818 Epoch 40: val_accuracy did not improve from 0.81818 Epoch 41: val_accuracy did not improve from 0.81818 Epoch 42: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 13.3420 - accuracy: 0.8182 - 24ms/epoch - 12ms/step ####################################################### the model mod18 use a learning rate = 5, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.50000 Epoch 4: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.50000 Epoch 6: val_accuracy did not improve from 0.50000 Epoch 7: val_accuracy did not improve from 0.50000 Epoch 8: val_accuracy did not improve from 0.50000 Epoch 9: val_accuracy did not improve from 0.50000 Epoch 10: val_accuracy did not improve from 0.50000 Epoch 11: val_accuracy did not improve from 0.50000 Epoch 12: val_accuracy did not improve from 0.50000 Epoch 13: val_accuracy did not improve from 0.50000 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy did not improve from 0.50000 Epoch 16: val_accuracy did not improve from 0.50000 Epoch 17: val_accuracy did not improve from 0.50000 Epoch 18: val_accuracy did not improve from 0.50000 Epoch 19: val_accuracy did not improve from 0.50000 Epoch 20: val_accuracy did not improve from 0.50000 Epoch 21: val_accuracy did not improve from 0.50000 Epoch 22: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 22.2522 - accuracy: 0.5000 - 25ms/epoch - 13ms/step ####################################################### the model mod19 use a learning rate = 6, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy did not improve from 0.54545 Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy did not improve from 0.54545 Epoch 8: val_accuracy did not improve from 0.54545 Epoch 9: val_accuracy did not improve from 0.54545 Epoch 10: val_accuracy did not improve from 0.54545 Epoch 11: val_accuracy did not improve from 0.54545 Epoch 12: val_accuracy did not improve from 0.54545 Epoch 13: val_accuracy did not improve from 0.54545 Epoch 14: val_accuracy did not improve from 0.54545 Epoch 15: val_accuracy did not improve from 0.54545 Epoch 16: val_accuracy did not improve from 0.54545 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy did not improve from 0.54545 Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 28.3889 - accuracy: 0.5455 - 25ms/epoch - 12ms/step ####################################################### the model mod20 use a learning rate = 7, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.56818 Epoch 3: val_accuracy did not improve from 0.56818 Epoch 4: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.56818 Epoch 6: val_accuracy did not improve from 0.56818 Epoch 7: val_accuracy did not improve from 0.56818 Epoch 8: val_accuracy did not improve from 0.56818 Epoch 9: val_accuracy did not improve from 0.56818 Epoch 10: val_accuracy did not improve from 0.56818 Epoch 11: val_accuracy did not improve from 0.56818 Epoch 12: val_accuracy did not improve from 0.56818 Epoch 13: val_accuracy did not improve from 0.56818 Epoch 14: val_accuracy did not improve from 0.56818 Epoch 15: val_accuracy did not improve from 0.56818 Epoch 16: val_accuracy did not improve from 0.56818 Epoch 17: val_accuracy did not improve from 0.56818 Epoch 18: val_accuracy did not improve from 0.56818 Epoch 19: val_accuracy did not improve from 0.56818 Epoch 20: val_accuracy did not improve from 0.56818 Epoch 21: val_accuracy did not improve from 0.56818
2/2 - 0s - loss: 28.6149 - accuracy: 0.5682 - 25ms/epoch - 13ms/step ####################################################### the model mod21 use a learning rate = 8, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.36364 Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364 Epoch 5: val_accuracy did not improve from 0.36364 Epoch 6: val_accuracy did not improve from 0.36364 Epoch 7: val_accuracy did not improve from 0.36364 Epoch 8: val_accuracy did not improve from 0.36364 Epoch 9: val_accuracy did not improve from 0.36364 Epoch 10: val_accuracy did not improve from 0.36364 Epoch 11: val_accuracy did not improve from 0.36364 Epoch 12: val_accuracy did not improve from 0.36364 Epoch 13: val_accuracy did not improve from 0.36364 Epoch 14: val_accuracy did not improve from 0.36364 Epoch 15: val_accuracy did not improve from 0.36364 Epoch 16: val_accuracy did not improve from 0.36364 Epoch 17: val_accuracy did not improve from 0.36364 Epoch 18: val_accuracy did not improve from 0.36364 Epoch 19: val_accuracy did not improve from 0.36364 Epoch 20: val_accuracy did not improve from 0.36364 Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 27.6002 - accuracy: 0.3636 - 31ms/epoch - 15ms/step ####################################################### the model mod22 use a learning rate = 9, l2 regularization = 0 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.54545 Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy did not improve from 0.54545 Epoch 8: val_accuracy did not improve from 0.54545 Epoch 9: val_accuracy did not improve from 0.54545 Epoch 10: val_accuracy did not improve from 0.54545 Epoch 11: val_accuracy did not improve from 0.54545 Epoch 12: val_accuracy did not improve from 0.54545 Epoch 13: val_accuracy did not improve from 0.54545 Epoch 14: val_accuracy did not improve from 0.54545 Epoch 15: val_accuracy did not improve from 0.54545 Epoch 16: val_accuracy did not improve from 0.54545 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy did not improve from 0.54545 Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 26.6883 - accuracy: 0.5455 - 23ms/epoch - 11ms/step ####################################################### the model mod23 use a learning rate = 0, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.36364 to 0.65909, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.65909 Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy improved from 0.65909 to 0.77273, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.77273 Epoch 7: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.81818 Epoch 9: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909 Epoch 43: val_accuracy did not improve from 0.90909 Epoch 44: val_accuracy did not improve from 0.90909 Epoch 45: val_accuracy did not improve from 0.90909 Epoch 46: val_accuracy did not improve from 0.90909 Epoch 47: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7387 - accuracy: 0.6364 - 44ms/epoch - 22ms/step ####################################################### the model mod24 use a learning rate = 1, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.29545 to 0.65909, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.65909 Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy improved from 0.65909 to 0.72727, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5599 - accuracy: 0.8182 - 24ms/epoch - 12ms/step ####################################################### the model mod25 use a learning rate = 2, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.56818 Epoch 3: val_accuracy improved from 0.56818 to 0.68182, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182 Epoch 6: val_accuracy did not improve from 0.68182 Epoch 7: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4932 - accuracy: 0.8864 - 25ms/epoch - 13ms/step ####################################################### the model mod26 use a learning rate = 3, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.50000 to 0.54545, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.56818 Epoch 5: val_accuracy improved from 0.56818 to 0.72727, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5983 - accuracy: 0.8636 - 36ms/epoch - 18ms/step ####################################################### the model mod27 use a learning rate = 4, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.63636 to 0.72727, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727 Epoch 5: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5402 - accuracy: 0.8182 - 23ms/epoch - 12ms/step ####################################################### the model mod28 use a learning rate = 5, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.59091 Epoch 3: val_accuracy did not improve from 0.59091 Epoch 4: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.59091 Epoch 6: val_accuracy did not improve from 0.59091 Epoch 7: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.65909 to 0.77273, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5508 - accuracy: 0.9091 - 24ms/epoch - 12ms/step ####################################################### the model mod29 use a learning rate = 6, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.63636 to 0.75000, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.75000 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5458 - accuracy: 0.7955 - 36ms/epoch - 18ms/step ####################################################### the model mod30 use a learning rate = 7, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.65909 Epoch 3: val_accuracy did not improve from 0.65909 Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.65909 Epoch 6: val_accuracy improved from 0.65909 to 0.75000, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.77273 Epoch 10: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5954 - accuracy: 0.8182 - 24ms/epoch - 12ms/step ####################################################### the model mod31 use a learning rate = 8, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.63636 Epoch 3: val_accuracy did not improve from 0.63636 Epoch 4: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy improved from 0.63636 to 0.72727, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.81818 Epoch 8: val_accuracy did not improve from 0.81818 Epoch 9: val_accuracy did not improve from 0.81818 Epoch 10: val_accuracy did not improve from 0.81818 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909 Epoch 43: val_accuracy did not improve from 0.90909 Epoch 44: val_accuracy did not improve from 0.90909 Epoch 45: val_accuracy did not improve from 0.90909 Epoch 46: val_accuracy did not improve from 0.90909 Epoch 47: val_accuracy did not improve from 0.90909 Epoch 48: val_accuracy did not improve from 0.90909 Epoch 49: val_accuracy did not improve from 0.90909 Epoch 50: val_accuracy did not improve from 0.90909 Epoch 51: val_accuracy did not improve from 0.90909 Epoch 52: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5716 - accuracy: 0.8409 - 55ms/epoch - 28ms/step ####################################################### the model mod32 use a learning rate = 9, l2 regularization = 0 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy improved from 0.90909 to 0.95455, saving model to best_model.h5 Epoch 28: val_accuracy did not improve from 0.95455 Epoch 29: val_accuracy did not improve from 0.95455 Epoch 30: val_accuracy did not improve from 0.95455 Epoch 31: val_accuracy did not improve from 0.95455 Epoch 32: val_accuracy did not improve from 0.95455 Epoch 33: val_accuracy did not improve from 0.95455 Epoch 34: val_accuracy did not improve from 0.95455 Epoch 35: val_accuracy did not improve from 0.95455 Epoch 36: val_accuracy did not improve from 0.95455 Epoch 37: val_accuracy did not improve from 0.95455 Epoch 38: val_accuracy did not improve from 0.95455 Epoch 39: val_accuracy did not improve from 0.95455 Epoch 40: val_accuracy did not improve from 0.95455 Epoch 41: val_accuracy did not improve from 0.95455 Epoch 42: val_accuracy did not improve from 0.95455 Epoch 43: val_accuracy did not improve from 0.95455 Epoch 44: val_accuracy did not improve from 0.95455 Epoch 45: val_accuracy did not improve from 0.95455 Epoch 46: val_accuracy did not improve from 0.95455 Epoch 47: val_accuracy did not improve from 0.95455
2/2 - 0s - loss: 0.5557 - accuracy: 0.8182 - 49ms/epoch - 25ms/step ####################################################### the model mod33 use a learning rate = 0, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.50000 Epoch 3: val_accuracy improved from 0.50000 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636 Epoch 35: val_accuracy did not improve from 0.88636 Epoch 36: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6896 - accuracy: 0.5455 - 23ms/epoch - 11ms/step ####################################################### the model mod34 use a learning rate = 1, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.70455 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4313 - accuracy: 0.8182 - 37ms/epoch - 18ms/step ####################################################### the model mod35 use a learning rate = 2, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.50000 to 0.75000, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909 Epoch 43: val_accuracy did not improve from 0.90909 Epoch 44: val_accuracy did not improve from 0.90909 Epoch 45: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3520 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod36 use a learning rate = 3, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.79545 Epoch 9: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.81818 Epoch 11: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy did not improve from 0.84091 Epoch 17: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636 Epoch 35: val_accuracy did not improve from 0.88636 Epoch 36: val_accuracy did not improve from 0.88636 Epoch 37: val_accuracy did not improve from 0.88636 Epoch 38: val_accuracy did not improve from 0.88636 Epoch 39: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.3347 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod37 use a learning rate = 4, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.59091 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.79545 Epoch 7: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3590 - accuracy: 0.8864 - 116ms/epoch - 58ms/step ####################################################### the model mod38 use a learning rate = 5, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.47727 to 0.54545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.61364 Epoch 8: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.63636 Epoch 10: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.70455 Epoch 13: val_accuracy did not improve from 0.70455 Epoch 14: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.72727 Epoch 16: val_accuracy did not improve from 0.72727 Epoch 17: val_accuracy did not improve from 0.72727 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.75000 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy did not improve from 0.75000 Epoch 25: val_accuracy did not improve from 0.75000 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy did not improve from 0.75000 Epoch 28: val_accuracy did not improve from 0.75000 Epoch 29: val_accuracy did not improve from 0.75000 Epoch 30: val_accuracy did not improve from 0.75000 Epoch 31: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy did not improve from 0.77273 Epoch 34: val_accuracy did not improve from 0.77273 Epoch 35: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 36: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 37: val_accuracy did not improve from 0.81818 Epoch 38: val_accuracy did not improve from 0.81818 Epoch 39: val_accuracy did not improve from 0.81818 Epoch 40: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 41: val_accuracy did not improve from 0.84091 Epoch 42: val_accuracy did not improve from 0.84091 Epoch 43: val_accuracy did not improve from 0.84091 Epoch 44: val_accuracy did not improve from 0.84091 Epoch 45: val_accuracy did not improve from 0.84091 Epoch 46: val_accuracy did not improve from 0.84091 Epoch 47: val_accuracy did not improve from 0.84091 Epoch 48: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 49: val_accuracy did not improve from 0.86364 Epoch 50: val_accuracy did not improve from 0.86364 Epoch 51: val_accuracy did not improve from 0.86364 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364 Epoch 55: val_accuracy did not improve from 0.86364 Epoch 56: val_accuracy did not improve from 0.86364 Epoch 57: val_accuracy did not improve from 0.86364 Epoch 58: val_accuracy did not improve from 0.86364 Epoch 59: val_accuracy did not improve from 0.86364 Epoch 60: val_accuracy did not improve from 0.86364 Epoch 61: val_accuracy did not improve from 0.86364 Epoch 62: val_accuracy did not improve from 0.86364 Epoch 63: val_accuracy did not improve from 0.86364 Epoch 64: val_accuracy did not improve from 0.86364 Epoch 65: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 66: val_accuracy did not improve from 0.88636 Epoch 67: val_accuracy did not improve from 0.88636 Epoch 68: val_accuracy did not improve from 0.88636 Epoch 69: val_accuracy did not improve from 0.88636 Epoch 70: val_accuracy did not improve from 0.88636 Epoch 71: val_accuracy did not improve from 0.88636 Epoch 72: val_accuracy did not improve from 0.88636 Epoch 73: val_accuracy did not improve from 0.88636 Epoch 74: val_accuracy did not improve from 0.88636 Epoch 75: val_accuracy did not improve from 0.88636 Epoch 76: val_accuracy did not improve from 0.88636 Epoch 77: val_accuracy did not improve from 0.88636 Epoch 78: val_accuracy did not improve from 0.88636 Epoch 79: val_accuracy did not improve from 0.88636 Epoch 80: val_accuracy did not improve from 0.88636 Epoch 81: val_accuracy did not improve from 0.88636 Epoch 82: val_accuracy did not improve from 0.88636 Epoch 83: val_accuracy did not improve from 0.88636 Epoch 84: val_accuracy did not improve from 0.88636 Epoch 85: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3546 - accuracy: 0.8864 - 34ms/epoch - 17ms/step ####################################################### the model mod39 use a learning rate = 6, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.18182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.18182 Epoch 3: val_accuracy did not improve from 0.18182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.18182 Epoch 5: val_accuracy did not improve from 0.18182 Epoch 6: val_accuracy did not improve from 0.18182 Epoch 7: val_accuracy did not improve from 0.18182 Epoch 8: val_accuracy did not improve from 0.18182 Epoch 9: val_accuracy did not improve from 0.18182 Epoch 10: val_accuracy did not improve from 0.18182 Epoch 11: val_accuracy did not improve from 0.18182 Epoch 12: val_accuracy did not improve from 0.18182 Epoch 13: val_accuracy did not improve from 0.18182 Epoch 14: val_accuracy did not improve from 0.18182 Epoch 15: val_accuracy did not improve from 0.18182 Epoch 16: val_accuracy did not improve from 0.18182 Epoch 17: val_accuracy did not improve from 0.18182 Epoch 18: val_accuracy did not improve from 0.18182 Epoch 19: val_accuracy did not improve from 0.18182 Epoch 20: val_accuracy did not improve from 0.18182 Epoch 21: val_accuracy did not improve from 0.18182
2/2 - 0s - loss: 3.1779 - accuracy: 0.1818 - 25ms/epoch - 13ms/step ####################################################### the model mod40 use a learning rate = 7, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.38636 Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy did not improve from 0.38636 Epoch 7: val_accuracy did not improve from 0.38636 Epoch 8: val_accuracy did not improve from 0.38636 Epoch 9: val_accuracy did not improve from 0.38636 Epoch 10: val_accuracy did not improve from 0.38636 Epoch 11: val_accuracy did not improve from 0.38636 Epoch 12: val_accuracy did not improve from 0.38636 Epoch 13: val_accuracy did not improve from 0.38636 Epoch 14: val_accuracy did not improve from 0.38636 Epoch 15: val_accuracy did not improve from 0.38636 Epoch 16: val_accuracy did not improve from 0.38636 Epoch 17: val_accuracy did not improve from 0.38636 Epoch 18: val_accuracy did not improve from 0.38636 Epoch 19: val_accuracy did not improve from 0.38636 Epoch 20: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.40909 Epoch 22: val_accuracy did not improve from 0.40909 Epoch 23: val_accuracy did not improve from 0.40909 Epoch 24: val_accuracy did not improve from 0.40909 Epoch 25: val_accuracy did not improve from 0.40909 Epoch 26: val_accuracy did not improve from 0.40909 Epoch 27: val_accuracy did not improve from 0.40909 Epoch 28: val_accuracy did not improve from 0.40909 Epoch 29: val_accuracy did not improve from 0.40909 Epoch 30: val_accuracy did not improve from 0.40909 Epoch 31: val_accuracy did not improve from 0.40909 Epoch 32: val_accuracy did not improve from 0.40909 Epoch 33: val_accuracy did not improve from 0.40909 Epoch 34: val_accuracy did not improve from 0.40909 Epoch 35: val_accuracy did not improve from 0.40909 Epoch 36: val_accuracy did not improve from 0.40909 Epoch 37: val_accuracy did not improve from 0.40909 Epoch 38: val_accuracy did not improve from 0.40909 Epoch 39: val_accuracy did not improve from 0.40909 Epoch 40: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 3.4290 - accuracy: 0.4091 - 38ms/epoch - 19ms/step ####################################################### the model mod41 use a learning rate = 8, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.52273 Epoch 3: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.52273 Epoch 5: val_accuracy did not improve from 0.52273 Epoch 6: val_accuracy did not improve from 0.52273 Epoch 7: val_accuracy did not improve from 0.52273 Epoch 8: val_accuracy did not improve from 0.52273 Epoch 9: val_accuracy did not improve from 0.52273 Epoch 10: val_accuracy did not improve from 0.52273 Epoch 11: val_accuracy did not improve from 0.52273 Epoch 12: val_accuracy did not improve from 0.52273 Epoch 13: val_accuracy did not improve from 0.52273 Epoch 14: val_accuracy did not improve from 0.52273 Epoch 15: val_accuracy did not improve from 0.52273 Epoch 16: val_accuracy did not improve from 0.52273 Epoch 17: val_accuracy did not improve from 0.52273 Epoch 18: val_accuracy did not improve from 0.52273 Epoch 19: val_accuracy did not improve from 0.52273 Epoch 20: val_accuracy did not improve from 0.52273 Epoch 21: val_accuracy did not improve from 0.52273
2/2 - 0s - loss: 3.7408 - accuracy: 0.5227 - 25ms/epoch - 12ms/step ####################################################### the model mod42 use a learning rate = 9, l2 regularization = 1 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.22727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.22727 Epoch 3: val_accuracy did not improve from 0.22727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.22727 Epoch 5: val_accuracy did not improve from 0.22727 Epoch 6: val_accuracy did not improve from 0.22727 Epoch 7: val_accuracy did not improve from 0.22727 Epoch 8: val_accuracy did not improve from 0.22727 Epoch 9: val_accuracy did not improve from 0.22727 Epoch 10: val_accuracy did not improve from 0.22727 Epoch 11: val_accuracy did not improve from 0.22727 Epoch 12: val_accuracy did not improve from 0.22727 Epoch 13: val_accuracy did not improve from 0.22727 Epoch 14: val_accuracy did not improve from 0.22727 Epoch 15: val_accuracy did not improve from 0.22727 Epoch 16: val_accuracy did not improve from 0.22727 Epoch 17: val_accuracy did not improve from 0.22727 Epoch 18: val_accuracy did not improve from 0.22727 Epoch 19: val_accuracy did not improve from 0.22727 Epoch 20: val_accuracy did not improve from 0.22727 Epoch 21: val_accuracy did not improve from 0.22727
2/2 - 0s - loss: 3.8253 - accuracy: 0.2273 - 24ms/epoch - 12ms/step ####################################################### the model mod43 use a learning rate = 0, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4630 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod44 use a learning rate = 1, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3539 - accuracy: 0.8864 - 43ms/epoch - 21ms/step ####################################################### the model mod45 use a learning rate = 2, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.79545 Epoch 10: val_accuracy did not improve from 0.79545 Epoch 11: val_accuracy did not improve from 0.79545 Epoch 12: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.81818 Epoch 14: val_accuracy did not improve from 0.81818 Epoch 15: val_accuracy did not improve from 0.81818 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy did not improve from 0.81818 Epoch 18: val_accuracy did not improve from 0.81818 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 27: val_accuracy did not improve from 0.84091 Epoch 28: val_accuracy did not improve from 0.84091 Epoch 29: val_accuracy did not improve from 0.84091 Epoch 30: val_accuracy did not improve from 0.84091 Epoch 31: val_accuracy did not improve from 0.84091 Epoch 32: val_accuracy did not improve from 0.84091 Epoch 33: val_accuracy did not improve from 0.84091 Epoch 34: val_accuracy did not improve from 0.84091 Epoch 35: val_accuracy did not improve from 0.84091 Epoch 36: val_accuracy did not improve from 0.84091 Epoch 37: val_accuracy did not improve from 0.84091 Epoch 38: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.86364 Epoch 40: val_accuracy did not improve from 0.86364 Epoch 41: val_accuracy did not improve from 0.86364 Epoch 42: val_accuracy did not improve from 0.86364 Epoch 43: val_accuracy did not improve from 0.86364 Epoch 44: val_accuracy did not improve from 0.86364 Epoch 45: val_accuracy did not improve from 0.86364 Epoch 46: val_accuracy did not improve from 0.86364 Epoch 47: val_accuracy did not improve from 0.86364 Epoch 48: val_accuracy did not improve from 0.86364 Epoch 49: val_accuracy did not improve from 0.86364 Epoch 50: val_accuracy did not improve from 0.86364 Epoch 51: val_accuracy did not improve from 0.86364 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364 Epoch 55: val_accuracy did not improve from 0.86364 Epoch 56: val_accuracy did not improve from 0.86364 Epoch 57: val_accuracy did not improve from 0.86364 Epoch 58: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 1.0730 - accuracy: 0.8636 - 25ms/epoch - 13ms/step ####################################################### the model mod46 use a learning rate = 3, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy did not improve from 0.54545 Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy did not improve from 0.54545 Epoch 8: val_accuracy did not improve from 0.54545 Epoch 9: val_accuracy did not improve from 0.54545 Epoch 10: val_accuracy did not improve from 0.54545 Epoch 11: val_accuracy did not improve from 0.54545 Epoch 12: val_accuracy did not improve from 0.54545 Epoch 13: val_accuracy did not improve from 0.54545 Epoch 14: val_accuracy did not improve from 0.54545 Epoch 15: val_accuracy did not improve from 0.54545 Epoch 16: val_accuracy did not improve from 0.54545 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy did not improve from 0.54545 Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 3.3518 - accuracy: 0.5455 - 23ms/epoch - 12ms/step ####################################################### the model mod47 use a learning rate = 4, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.43182 Epoch 6: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.50000 Epoch 10: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.56818 Epoch 13: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.59091 Epoch 15: val_accuracy did not improve from 0.59091 Epoch 16: val_accuracy did not improve from 0.59091 Epoch 17: val_accuracy did not improve from 0.59091 Epoch 18: val_accuracy did not improve from 0.59091 Epoch 19: val_accuracy did not improve from 0.59091 Epoch 20: val_accuracy did not improve from 0.59091 Epoch 21: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.63636 Epoch 23: val_accuracy did not improve from 0.63636 Epoch 24: val_accuracy did not improve from 0.63636 Epoch 25: val_accuracy did not improve from 0.63636 Epoch 26: val_accuracy did not improve from 0.63636 Epoch 27: val_accuracy did not improve from 0.63636 Epoch 28: val_accuracy did not improve from 0.63636 Epoch 29: val_accuracy did not improve from 0.63636 Epoch 30: val_accuracy did not improve from 0.63636 Epoch 31: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.65909 Epoch 33: val_accuracy did not improve from 0.65909 Epoch 34: val_accuracy did not improve from 0.65909 Epoch 35: val_accuracy did not improve from 0.65909 Epoch 36: val_accuracy did not improve from 0.65909 Epoch 37: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 38: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.70455 Epoch 40: val_accuracy did not improve from 0.70455 Epoch 41: val_accuracy did not improve from 0.70455 Epoch 42: val_accuracy did not improve from 0.70455 Epoch 43: val_accuracy did not improve from 0.70455 Epoch 44: val_accuracy did not improve from 0.70455 Epoch 45: val_accuracy did not improve from 0.70455 Epoch 46: val_accuracy did not improve from 0.70455 Epoch 47: val_accuracy did not improve from 0.70455 Epoch 48: val_accuracy did not improve from 0.70455 Epoch 49: val_accuracy did not improve from 0.70455 Epoch 50: val_accuracy did not improve from 0.70455 Epoch 51: val_accuracy did not improve from 0.70455 Epoch 52: val_accuracy did not improve from 0.70455 Epoch 53: val_accuracy did not improve from 0.70455 Epoch 54: val_accuracy did not improve from 0.70455 Epoch 55: val_accuracy did not improve from 0.70455 Epoch 56: val_accuracy did not improve from 0.70455 Epoch 57: val_accuracy did not improve from 0.70455 Epoch 58: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 1.8862 - accuracy: 0.7045 - 24ms/epoch - 12ms/step ####################################################### the model mod48 use a learning rate = 5, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.40909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.40909 Epoch 3: val_accuracy did not improve from 0.40909 Epoch 4: val_accuracy did not improve from 0.40909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.40909 Epoch 6: val_accuracy did not improve from 0.40909 Epoch 7: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.43182 Epoch 9: val_accuracy did not improve from 0.43182 Epoch 10: val_accuracy did not improve from 0.43182 Epoch 11: val_accuracy did not improve from 0.43182 Epoch 12: val_accuracy did not improve from 0.43182 Epoch 13: val_accuracy did not improve from 0.43182 Epoch 14: val_accuracy did not improve from 0.43182 Epoch 15: val_accuracy did not improve from 0.43182 Epoch 16: val_accuracy did not improve from 0.43182 Epoch 17: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.45455 Epoch 19: val_accuracy did not improve from 0.45455 Epoch 20: val_accuracy did not improve from 0.45455 Epoch 21: val_accuracy did not improve from 0.45455 Epoch 22: val_accuracy did not improve from 0.45455 Epoch 23: val_accuracy did not improve from 0.45455 Epoch 24: val_accuracy did not improve from 0.45455 Epoch 25: val_accuracy did not improve from 0.45455 Epoch 26: val_accuracy did not improve from 0.45455 Epoch 27: val_accuracy did not improve from 0.45455 Epoch 28: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 29: val_accuracy did not improve from 0.47727 Epoch 30: val_accuracy did not improve from 0.47727 Epoch 31: val_accuracy did not improve from 0.47727 Epoch 32: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 33: val_accuracy did not improve from 0.50000 Epoch 34: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.52273 Epoch 36: val_accuracy did not improve from 0.52273 Epoch 37: val_accuracy did not improve from 0.52273 Epoch 38: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.56818 Epoch 40: val_accuracy did not improve from 0.56818 Epoch 41: val_accuracy did not improve from 0.56818 Epoch 42: val_accuracy did not improve from 0.56818 Epoch 43: val_accuracy did not improve from 0.56818 Epoch 44: val_accuracy did not improve from 0.56818 Epoch 45: val_accuracy did not improve from 0.56818 Epoch 46: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 47: val_accuracy did not improve from 0.59091 Epoch 48: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 49: val_accuracy did not improve from 0.61364 Epoch 50: val_accuracy did not improve from 0.61364 Epoch 51: val_accuracy did not improve from 0.61364 Epoch 52: val_accuracy did not improve from 0.61364 Epoch 53: val_accuracy did not improve from 0.61364 Epoch 54: val_accuracy did not improve from 0.61364 Epoch 55: val_accuracy did not improve from 0.61364 Epoch 56: val_accuracy did not improve from 0.61364 Epoch 57: val_accuracy did not improve from 0.61364 Epoch 58: val_accuracy did not improve from 0.61364 Epoch 59: val_accuracy did not improve from 0.61364 Epoch 60: val_accuracy did not improve from 0.61364 Epoch 61: val_accuracy did not improve from 0.61364 Epoch 62: val_accuracy did not improve from 0.61364 Epoch 63: val_accuracy did not improve from 0.61364 Epoch 64: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 65: val_accuracy did not improve from 0.63636 Epoch 66: val_accuracy did not improve from 0.63636 Epoch 67: val_accuracy did not improve from 0.63636 Epoch 68: val_accuracy did not improve from 0.63636 Epoch 69: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 70: val_accuracy did not improve from 0.65909 Epoch 71: val_accuracy did not improve from 0.65909 Epoch 72: val_accuracy did not improve from 0.65909 Epoch 73: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 74: val_accuracy did not improve from 0.68182 Epoch 75: val_accuracy did not improve from 0.68182 Epoch 76: val_accuracy did not improve from 0.68182 Epoch 77: val_accuracy did not improve from 0.68182 Epoch 78: val_accuracy did not improve from 0.68182 Epoch 79: val_accuracy did not improve from 0.68182 Epoch 80: val_accuracy did not improve from 0.68182 Epoch 81: val_accuracy did not improve from 0.68182 Epoch 82: val_accuracy did not improve from 0.68182 Epoch 83: val_accuracy did not improve from 0.68182 Epoch 84: val_accuracy did not improve from 0.68182 Epoch 85: val_accuracy did not improve from 0.68182 Epoch 86: val_accuracy did not improve from 0.68182 Epoch 87: val_accuracy did not improve from 0.68182 Epoch 88: val_accuracy did not improve from 0.68182 Epoch 89: val_accuracy did not improve from 0.68182 Epoch 90: val_accuracy did not improve from 0.68182 Epoch 91: val_accuracy did not improve from 0.68182 Epoch 92: val_accuracy did not improve from 0.68182 Epoch 93: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 2.2558 - accuracy: 0.6818 - 24ms/epoch - 12ms/step ####################################################### the model mod49 use a learning rate = 6, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.61364 Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364 Epoch 5: val_accuracy did not improve from 0.61364 Epoch 6: val_accuracy did not improve from 0.61364 Epoch 7: val_accuracy did not improve from 0.61364 Epoch 8: val_accuracy did not improve from 0.61364 Epoch 9: val_accuracy did not improve from 0.61364 Epoch 10: val_accuracy did not improve from 0.61364 Epoch 11: val_accuracy did not improve from 0.61364 Epoch 12: val_accuracy did not improve from 0.61364 Epoch 13: val_accuracy did not improve from 0.61364 Epoch 14: val_accuracy did not improve from 0.61364 Epoch 15: val_accuracy did not improve from 0.61364 Epoch 16: val_accuracy did not improve from 0.61364 Epoch 17: val_accuracy did not improve from 0.61364 Epoch 18: val_accuracy did not improve from 0.61364 Epoch 19: val_accuracy did not improve from 0.61364 Epoch 20: val_accuracy did not improve from 0.61364 Epoch 21: val_accuracy did not improve from 0.61364
2/2 - 0s - loss: 3.1395 - accuracy: 0.6136 - 24ms/epoch - 12ms/step ####################################################### the model mod50 use a learning rate = 7, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.31818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.31818 Epoch 3: val_accuracy did not improve from 0.31818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.31818 Epoch 5: val_accuracy did not improve from 0.31818 Epoch 6: val_accuracy did not improve from 0.31818 Epoch 7: val_accuracy did not improve from 0.31818 Epoch 8: val_accuracy did not improve from 0.31818 Epoch 9: val_accuracy did not improve from 0.31818 Epoch 10: val_accuracy did not improve from 0.31818 Epoch 11: val_accuracy did not improve from 0.31818 Epoch 12: val_accuracy did not improve from 0.31818 Epoch 13: val_accuracy did not improve from 0.31818 Epoch 14: val_accuracy did not improve from 0.31818 Epoch 15: val_accuracy did not improve from 0.31818 Epoch 16: val_accuracy did not improve from 0.31818 Epoch 17: val_accuracy did not improve from 0.31818 Epoch 18: val_accuracy did not improve from 0.31818 Epoch 19: val_accuracy did not improve from 0.31818 Epoch 20: val_accuracy did not improve from 0.31818 Epoch 21: val_accuracy did not improve from 0.31818
2/2 - 0s - loss: 3.5574 - accuracy: 0.3182 - 48ms/epoch - 24ms/step ####################################################### the model mod51 use a learning rate = 8, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.75000 Epoch 4: val_accuracy did not improve from 0.75000 Epoch 5: val_accuracy did not improve from 0.75000 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy did not improve from 0.75000 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy did not improve from 0.75000 Epoch 15: val_accuracy did not improve from 0.75000 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 3.0881 - accuracy: 0.7500 - 23ms/epoch - 12ms/step ####################################################### the model mod52 use a learning rate = 9, l2 regularization = 1 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.50000 Epoch 3: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.50000 Epoch 5: val_accuracy did not improve from 0.50000 Epoch 6: val_accuracy did not improve from 0.50000 Epoch 7: val_accuracy did not improve from 0.50000 Epoch 8: val_accuracy did not improve from 0.50000 Epoch 9: val_accuracy did not improve from 0.50000 Epoch 10: val_accuracy did not improve from 0.50000 Epoch 11: val_accuracy did not improve from 0.50000 Epoch 12: val_accuracy did not improve from 0.50000 Epoch 13: val_accuracy did not improve from 0.50000 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy did not improve from 0.50000 Epoch 16: val_accuracy did not improve from 0.50000 Epoch 17: val_accuracy did not improve from 0.50000 Epoch 18: val_accuracy did not improve from 0.50000 Epoch 19: val_accuracy did not improve from 0.50000 Epoch 20: val_accuracy did not improve from 0.50000 Epoch 21: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 3.8460 - accuracy: 0.5000 - 37ms/epoch - 18ms/step ####################################################### the model mod53 use a learning rate = 0, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4138 - accuracy: 0.8636 - 24ms/epoch - 12ms/step ####################################################### the model mod54 use a learning rate = 1, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3810 - accuracy: 0.8182 - 25ms/epoch - 12ms/step ####################################################### the model mod55 use a learning rate = 2, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182 Epoch 28: val_accuracy did not improve from 0.93182 Epoch 29: val_accuracy did not improve from 0.93182 Epoch 30: val_accuracy did not improve from 0.93182 Epoch 31: val_accuracy did not improve from 0.93182 Epoch 32: val_accuracy did not improve from 0.93182 Epoch 33: val_accuracy did not improve from 0.93182 Epoch 34: val_accuracy did not improve from 0.93182 Epoch 35: val_accuracy did not improve from 0.93182 Epoch 36: val_accuracy did not improve from 0.93182 Epoch 37: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.3533 - accuracy: 0.8864 - 24ms/epoch - 12ms/step ####################################################### the model mod56 use a learning rate = 3, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.90909 Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3430 - accuracy: 0.8864 - 42ms/epoch - 21ms/step ####################################################### the model mod57 use a learning rate = 4, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3799 - accuracy: 0.8409 - 24ms/epoch - 12ms/step ####################################################### the model mod58 use a learning rate = 5, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182 Epoch 28: val_accuracy did not improve from 0.93182 Epoch 29: val_accuracy did not improve from 0.93182 Epoch 30: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4089 - accuracy: 0.8182 - 38ms/epoch - 19ms/step ####################################################### the model mod59 use a learning rate = 6, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.70455 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.88636 Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy improved from 0.90909 to 0.95455, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.95455 Epoch 23: val_accuracy did not improve from 0.95455 Epoch 24: val_accuracy did not improve from 0.95455 Epoch 25: val_accuracy did not improve from 0.95455 Epoch 26: val_accuracy did not improve from 0.95455 Epoch 27: val_accuracy did not improve from 0.95455 Epoch 28: val_accuracy did not improve from 0.95455 Epoch 29: val_accuracy did not improve from 0.95455 Epoch 30: val_accuracy did not improve from 0.95455 Epoch 31: val_accuracy did not improve from 0.95455 Epoch 32: val_accuracy did not improve from 0.95455 Epoch 33: val_accuracy did not improve from 0.95455 Epoch 34: val_accuracy did not improve from 0.95455 Epoch 35: val_accuracy did not improve from 0.95455 Epoch 36: val_accuracy did not improve from 0.95455 Epoch 37: val_accuracy did not improve from 0.95455 Epoch 38: val_accuracy did not improve from 0.95455 Epoch 39: val_accuracy did not improve from 0.95455 Epoch 40: val_accuracy did not improve from 0.95455 Epoch 41: val_accuracy did not improve from 0.95455
2/2 - 0s - loss: 0.4011 - accuracy: 0.8182 - 34ms/epoch - 17ms/step ####################################################### the model mod60 use a learning rate = 7, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.90909 Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3912 - accuracy: 0.9091 - 28ms/epoch - 14ms/step ####################################################### the model mod61 use a learning rate = 8, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182 Epoch 28: val_accuracy did not improve from 0.93182 Epoch 29: val_accuracy did not improve from 0.93182 Epoch 30: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4415 - accuracy: 0.8182 - 42ms/epoch - 21ms/step ####################################################### the model mod62 use a learning rate = 9, l2 regularization = 1 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4065 - accuracy: 0.8409 - 24ms/epoch - 12ms/step ####################################################### the model mod63 use a learning rate = 0, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.81818 Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818 Epoch 5: val_accuracy did not improve from 0.81818 Epoch 6: val_accuracy did not improve from 0.81818 Epoch 7: val_accuracy did not improve from 0.81818 Epoch 8: val_accuracy did not improve from 0.81818 Epoch 9: val_accuracy did not improve from 0.81818 Epoch 10: val_accuracy did not improve from 0.81818 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy did not improve from 0.81818 Epoch 13: val_accuracy did not improve from 0.81818 Epoch 14: val_accuracy did not improve from 0.81818 Epoch 15: val_accuracy did not improve from 0.81818 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy did not improve from 0.81818 Epoch 18: val_accuracy did not improve from 0.81818 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.6966 - accuracy: 0.5455 - 26ms/epoch - 13ms/step ####################################################### the model mod64 use a learning rate = 1, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.90909 Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7001 - accuracy: 0.8182 - 27ms/epoch - 14ms/step ####################################################### the model mod65 use a learning rate = 2, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.84091 Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636 Epoch 35: val_accuracy did not improve from 0.88636 Epoch 36: val_accuracy did not improve from 0.88636 Epoch 37: val_accuracy did not improve from 0.88636 Epoch 38: val_accuracy did not improve from 0.88636 Epoch 39: val_accuracy did not improve from 0.88636 Epoch 40: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4157 - accuracy: 0.8636 - 25ms/epoch - 12ms/step ####################################################### the model mod66 use a learning rate = 3, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.43182 Epoch 3: val_accuracy did not improve from 0.43182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.43182 Epoch 5: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.45455 to 0.52273, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.52273 Epoch 8: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 13: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 14: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.77273 Epoch 19: val_accuracy did not improve from 0.77273 Epoch 20: val_accuracy did not improve from 0.77273 Epoch 21: val_accuracy did not improve from 0.77273 Epoch 22: val_accuracy did not improve from 0.77273 Epoch 23: val_accuracy did not improve from 0.77273 Epoch 24: val_accuracy did not improve from 0.77273 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 27: val_accuracy did not improve from 0.79545 Epoch 28: val_accuracy did not improve from 0.79545 Epoch 29: val_accuracy did not improve from 0.79545 Epoch 30: val_accuracy did not improve from 0.79545 Epoch 31: val_accuracy did not improve from 0.79545 Epoch 32: val_accuracy did not improve from 0.79545 Epoch 33: val_accuracy did not improve from 0.79545 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy did not improve from 0.79545 Epoch 38: val_accuracy did not improve from 0.79545 Epoch 39: val_accuracy did not improve from 0.79545 Epoch 40: val_accuracy did not improve from 0.79545 Epoch 41: val_accuracy did not improve from 0.79545 Epoch 42: val_accuracy did not improve from 0.79545 Epoch 43: val_accuracy did not improve from 0.79545 Epoch 44: val_accuracy did not improve from 0.79545 Epoch 45: val_accuracy did not improve from 0.79545 Epoch 46: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.6703 - accuracy: 0.7955 - 26ms/epoch - 13ms/step ####################################################### the model mod67 use a learning rate = 4, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.81818 Epoch 6: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3950 - accuracy: 0.8864 - 29ms/epoch - 15ms/step ####################################################### the model mod68 use a learning rate = 5, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.61364 to 0.65909, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3984 - accuracy: 0.8636 - 46ms/epoch - 23ms/step ####################################################### the model mod69 use a learning rate = 6, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.65909 Epoch 5: val_accuracy did not improve from 0.65909 Epoch 6: val_accuracy did not improve from 0.65909 Epoch 7: val_accuracy did not improve from 0.65909 Epoch 8: val_accuracy did not improve from 0.65909 Epoch 9: val_accuracy did not improve from 0.65909 Epoch 10: val_accuracy did not improve from 0.65909 Epoch 11: val_accuracy did not improve from 0.65909 Epoch 12: val_accuracy did not improve from 0.65909 Epoch 13: val_accuracy did not improve from 0.65909 Epoch 14: val_accuracy did not improve from 0.65909 Epoch 15: val_accuracy did not improve from 0.65909 Epoch 16: val_accuracy did not improve from 0.65909 Epoch 17: val_accuracy did not improve from 0.65909 Epoch 18: val_accuracy did not improve from 0.65909 Epoch 19: val_accuracy did not improve from 0.65909 Epoch 20: val_accuracy did not improve from 0.65909 Epoch 21: val_accuracy did not improve from 0.65909 Epoch 22: val_accuracy did not improve from 0.65909 Epoch 23: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.9318 - accuracy: 0.6591 - 30ms/epoch - 15ms/step ####################################################### the model mod70 use a learning rate = 7, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.75000 Epoch 3: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000 Epoch 5: val_accuracy did not improve from 0.75000 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy did not improve from 0.75000 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy did not improve from 0.75000 Epoch 15: val_accuracy did not improve from 0.75000 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.8926 - accuracy: 0.7500 - 34ms/epoch - 17ms/step ####################################################### the model mod71 use a learning rate = 8, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.27273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.27273 Epoch 3: val_accuracy did not improve from 0.27273 Epoch 4: val_accuracy did not improve from 0.27273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.27273 Epoch 6: val_accuracy did not improve from 0.27273 Epoch 7: val_accuracy did not improve from 0.27273 Epoch 8: val_accuracy did not improve from 0.27273 Epoch 9: val_accuracy did not improve from 0.27273 Epoch 10: val_accuracy did not improve from 0.27273 Epoch 11: val_accuracy did not improve from 0.27273 Epoch 12: val_accuracy did not improve from 0.27273 Epoch 13: val_accuracy did not improve from 0.27273 Epoch 14: val_accuracy did not improve from 0.27273 Epoch 15: val_accuracy did not improve from 0.27273 Epoch 16: val_accuracy did not improve from 0.27273 Epoch 17: val_accuracy did not improve from 0.27273 Epoch 18: val_accuracy did not improve from 0.27273 Epoch 19: val_accuracy did not improve from 0.27273 Epoch 20: val_accuracy did not improve from 0.27273 Epoch 21: val_accuracy did not improve from 0.27273
2/2 - 0s - loss: 1.2854 - accuracy: 0.2727 - 28ms/epoch - 14ms/step ####################################################### the model mod72 use a learning rate = 9, l2 regularization = 2 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.77273 Epoch 3: val_accuracy did not improve from 0.77273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.77273 Epoch 5: val_accuracy did not improve from 0.77273 Epoch 6: val_accuracy did not improve from 0.77273 Epoch 7: val_accuracy did not improve from 0.77273 Epoch 8: val_accuracy did not improve from 0.77273 Epoch 9: val_accuracy did not improve from 0.77273 Epoch 10: val_accuracy did not improve from 0.77273 Epoch 11: val_accuracy did not improve from 0.77273 Epoch 12: val_accuracy did not improve from 0.77273 Epoch 13: val_accuracy did not improve from 0.77273 Epoch 14: val_accuracy did not improve from 0.77273 Epoch 15: val_accuracy did not improve from 0.77273 Epoch 16: val_accuracy did not improve from 0.77273 Epoch 17: val_accuracy did not improve from 0.77273 Epoch 18: val_accuracy did not improve from 0.77273 Epoch 19: val_accuracy did not improve from 0.77273 Epoch 20: val_accuracy did not improve from 0.77273 Epoch 21: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.8863 - accuracy: 0.7727 - 51ms/epoch - 26ms/step ####################################################### the model mod73 use a learning rate = 0, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.88636 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4764 - accuracy: 0.7955 - 31ms/epoch - 16ms/step ####################################################### the model mod74 use a learning rate = 1, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.59091 to 0.70455, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727 Epoch 5: val_accuracy did not improve from 0.72727 Epoch 6: val_accuracy did not improve from 0.72727 Epoch 7: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.77273 Epoch 10: val_accuracy did not improve from 0.77273 Epoch 11: val_accuracy did not improve from 0.77273 Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.79545 Epoch 14: val_accuracy did not improve from 0.79545 Epoch 15: val_accuracy did not improve from 0.79545 Epoch 16: val_accuracy did not improve from 0.79545 Epoch 17: val_accuracy did not improve from 0.79545 Epoch 18: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818 Epoch 31: val_accuracy did not improve from 0.81818 Epoch 32: val_accuracy did not improve from 0.81818 Epoch 33: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 34: val_accuracy did not improve from 0.84091 Epoch 35: val_accuracy did not improve from 0.84091 Epoch 36: val_accuracy did not improve from 0.84091 Epoch 37: val_accuracy did not improve from 0.84091 Epoch 38: val_accuracy did not improve from 0.84091 Epoch 39: val_accuracy did not improve from 0.84091 Epoch 40: val_accuracy did not improve from 0.84091 Epoch 41: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 42: val_accuracy did not improve from 0.86364 Epoch 43: val_accuracy did not improve from 0.86364 Epoch 44: val_accuracy did not improve from 0.86364 Epoch 45: val_accuracy did not improve from 0.86364 Epoch 46: val_accuracy did not improve from 0.86364 Epoch 47: val_accuracy did not improve from 0.86364 Epoch 48: val_accuracy did not improve from 0.86364 Epoch 49: val_accuracy did not improve from 0.86364 Epoch 50: val_accuracy did not improve from 0.86364 Epoch 51: val_accuracy did not improve from 0.86364 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364 Epoch 55: val_accuracy did not improve from 0.86364 Epoch 56: val_accuracy did not improve from 0.86364 Epoch 57: val_accuracy did not improve from 0.86364 Epoch 58: val_accuracy did not improve from 0.86364 Epoch 59: val_accuracy did not improve from 0.86364 Epoch 60: val_accuracy did not improve from 0.86364 Epoch 61: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4447 - accuracy: 0.8409 - 50ms/epoch - 25ms/step ####################################################### the model mod75 use a learning rate = 2, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.59091 Epoch 4: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.61364 Epoch 6: val_accuracy did not improve from 0.61364 Epoch 7: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.65909 Epoch 10: val_accuracy did not improve from 0.65909 Epoch 11: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.68182 Epoch 13: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.70455 Epoch 15: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.77273 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 38: val_accuracy did not improve from 0.81818 Epoch 39: val_accuracy did not improve from 0.81818 Epoch 40: val_accuracy did not improve from 0.81818 Epoch 41: val_accuracy did not improve from 0.81818 Epoch 42: val_accuracy did not improve from 0.81818 Epoch 43: val_accuracy did not improve from 0.81818 Epoch 44: val_accuracy did not improve from 0.81818 Epoch 45: val_accuracy did not improve from 0.81818 Epoch 46: val_accuracy did not improve from 0.81818 Epoch 47: val_accuracy did not improve from 0.81818 Epoch 48: val_accuracy did not improve from 0.81818 Epoch 49: val_accuracy did not improve from 0.81818 Epoch 50: val_accuracy did not improve from 0.81818 Epoch 51: val_accuracy did not improve from 0.81818 Epoch 52: val_accuracy did not improve from 0.81818 Epoch 53: val_accuracy did not improve from 0.81818 Epoch 54: val_accuracy did not improve from 0.81818 Epoch 55: val_accuracy did not improve from 0.81818 Epoch 56: val_accuracy did not improve from 0.81818 Epoch 57: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.6486 - accuracy: 0.8182 - 34ms/epoch - 17ms/step ####################################################### the model mod76 use a learning rate = 3, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.59091 Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091 Epoch 5: val_accuracy did not improve from 0.59091 Epoch 6: val_accuracy did not improve from 0.59091 Epoch 7: val_accuracy did not improve from 0.59091 Epoch 8: val_accuracy did not improve from 0.59091 Epoch 9: val_accuracy did not improve from 0.59091 Epoch 10: val_accuracy did not improve from 0.59091 Epoch 11: val_accuracy did not improve from 0.59091 Epoch 12: val_accuracy did not improve from 0.59091 Epoch 13: val_accuracy did not improve from 0.59091 Epoch 14: val_accuracy did not improve from 0.59091 Epoch 15: val_accuracy did not improve from 0.59091 Epoch 16: val_accuracy did not improve from 0.59091 Epoch 17: val_accuracy did not improve from 0.59091 Epoch 18: val_accuracy did not improve from 0.59091 Epoch 19: val_accuracy did not improve from 0.59091 Epoch 20: val_accuracy did not improve from 0.59091 Epoch 21: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 1.1338 - accuracy: 0.5909 - 36ms/epoch - 18ms/step ####################################################### the model mod77 use a learning rate = 4, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.63636 Epoch 3: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.65909 Epoch 5: val_accuracy did not improve from 0.65909 Epoch 6: val_accuracy did not improve from 0.65909 Epoch 7: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.72727 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy did not improve from 0.72727 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy did not improve from 0.72727 Epoch 16: val_accuracy did not improve from 0.72727 Epoch 17: val_accuracy did not improve from 0.72727 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy did not improve from 0.72727 Epoch 21: val_accuracy did not improve from 0.72727 Epoch 22: val_accuracy did not improve from 0.72727 Epoch 23: val_accuracy did not improve from 0.72727 Epoch 24: val_accuracy did not improve from 0.72727 Epoch 25: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy did not improve from 0.75000 Epoch 28: val_accuracy did not improve from 0.75000 Epoch 29: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 30: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 31: val_accuracy did not improve from 0.79545 Epoch 32: val_accuracy did not improve from 0.79545 Epoch 33: val_accuracy did not improve from 0.79545 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy did not improve from 0.79545 Epoch 38: val_accuracy did not improve from 0.79545 Epoch 39: val_accuracy did not improve from 0.79545 Epoch 40: val_accuracy did not improve from 0.79545 Epoch 41: val_accuracy did not improve from 0.79545 Epoch 42: val_accuracy did not improve from 0.79545 Epoch 43: val_accuracy did not improve from 0.79545 Epoch 44: val_accuracy did not improve from 0.79545 Epoch 45: val_accuracy did not improve from 0.79545 Epoch 46: val_accuracy did not improve from 0.79545 Epoch 47: val_accuracy did not improve from 0.79545 Epoch 48: val_accuracy did not improve from 0.79545 Epoch 49: val_accuracy did not improve from 0.79545 Epoch 50: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.7364 - accuracy: 0.7955 - 33ms/epoch - 17ms/step ####################################################### the model mod78 use a learning rate = 5, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.72727 Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.75000 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy did not improve from 0.75000 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.77273 Epoch 15: val_accuracy did not improve from 0.77273 Epoch 16: val_accuracy did not improve from 0.77273 Epoch 17: val_accuracy did not improve from 0.77273 Epoch 18: val_accuracy did not improve from 0.77273 Epoch 19: val_accuracy did not improve from 0.77273 Epoch 20: val_accuracy did not improve from 0.77273 Epoch 21: val_accuracy did not improve from 0.77273 Epoch 22: val_accuracy did not improve from 0.77273 Epoch 23: val_accuracy did not improve from 0.77273 Epoch 24: val_accuracy did not improve from 0.77273 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.7695 - accuracy: 0.7727 - 36ms/epoch - 18ms/step ####################################################### the model mod79 use a learning rate = 6, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.84091 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy did not improve from 0.84091 Epoch 17: val_accuracy did not improve from 0.84091 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.9676 - accuracy: 0.8409 - 30ms/epoch - 15ms/step ####################################################### the model mod80 use a learning rate = 7, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545 Epoch 3: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.54545 Epoch 5: val_accuracy did not improve from 0.54545 Epoch 6: val_accuracy did not improve from 0.54545 Epoch 7: val_accuracy did not improve from 0.54545 Epoch 8: val_accuracy did not improve from 0.54545 Epoch 9: val_accuracy did not improve from 0.54545 Epoch 10: val_accuracy did not improve from 0.54545 Epoch 11: val_accuracy did not improve from 0.54545 Epoch 12: val_accuracy did not improve from 0.54545 Epoch 13: val_accuracy did not improve from 0.54545 Epoch 14: val_accuracy did not improve from 0.54545 Epoch 15: val_accuracy did not improve from 0.54545 Epoch 16: val_accuracy did not improve from 0.54545 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy did not improve from 0.54545 Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 0.9429 - accuracy: 0.5455 - 55ms/epoch - 27ms/step ####################################################### the model mod81 use a learning rate = 8, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.47727 Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727 Epoch 5: val_accuracy did not improve from 0.47727 Epoch 6: val_accuracy did not improve from 0.47727 Epoch 7: val_accuracy did not improve from 0.47727 Epoch 8: val_accuracy did not improve from 0.47727 Epoch 9: val_accuracy did not improve from 0.47727 Epoch 10: val_accuracy did not improve from 0.47727 Epoch 11: val_accuracy did not improve from 0.47727 Epoch 12: val_accuracy did not improve from 0.47727 Epoch 13: val_accuracy did not improve from 0.47727 Epoch 14: val_accuracy did not improve from 0.47727 Epoch 15: val_accuracy did not improve from 0.47727 Epoch 16: val_accuracy did not improve from 0.47727 Epoch 17: val_accuracy did not improve from 0.47727 Epoch 18: val_accuracy did not improve from 0.47727 Epoch 19: val_accuracy did not improve from 0.47727 Epoch 20: val_accuracy did not improve from 0.47727 Epoch 21: val_accuracy did not improve from 0.47727
2/2 - 0s - loss: 1.1341 - accuracy: 0.4773 - 34ms/epoch - 17ms/step ####################################################### the model mod82 use a learning rate = 9, l2 regularization = 2 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.90909 Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.8586 - accuracy: 0.9091 - 31ms/epoch - 16ms/step ####################################################### the model mod83 use a learning rate = 0, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3791 - accuracy: 0.8409 - 35ms/epoch - 17ms/step ####################################################### the model mod84 use a learning rate = 1, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3972 - accuracy: 0.8864 - 31ms/epoch - 16ms/step ####################################################### the model mod85 use a learning rate = 2, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.90909, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.93182 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4254 - accuracy: 0.8409 - 36ms/epoch - 18ms/step ####################################################### the model mod86 use a learning rate = 3, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4639 - accuracy: 0.8182 - 35ms/epoch - 18ms/step ####################################################### the model mod87 use a learning rate = 4, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4546 - accuracy: 0.8864 - 37ms/epoch - 18ms/step ####################################################### the model mod88 use a learning rate = 5, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636 Epoch 35: val_accuracy did not improve from 0.88636 Epoch 36: val_accuracy did not improve from 0.88636 Epoch 37: val_accuracy did not improve from 0.88636 Epoch 38: val_accuracy did not improve from 0.88636 Epoch 39: val_accuracy did not improve from 0.88636 Epoch 40: val_accuracy did not improve from 0.88636 Epoch 41: val_accuracy did not improve from 0.88636 Epoch 42: val_accuracy did not improve from 0.88636 Epoch 43: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5924 - accuracy: 0.8409 - 37ms/epoch - 18ms/step ####################################################### the model mod89 use a learning rate = 6, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.81818 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4077 - accuracy: 0.8182 - 35ms/epoch - 18ms/step ####################################################### the model mod90 use a learning rate = 7, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4023 - accuracy: 0.8636 - 34ms/epoch - 17ms/step ####################################################### the model mod91 use a learning rate = 8, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.88636 Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4366 - accuracy: 0.8409 - 34ms/epoch - 17ms/step ####################################################### the model mod92 use a learning rate = 9, l2 regularization = 2 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.79545 Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4378 - accuracy: 0.8636 - 34ms/epoch - 17ms/step ####################################################### the model mod93 use a learning rate = 0, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.79545 Epoch 3: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.79545 Epoch 5: val_accuracy did not improve from 0.79545 Epoch 6: val_accuracy did not improve from 0.79545 Epoch 7: val_accuracy did not improve from 0.79545 Epoch 8: val_accuracy did not improve from 0.79545 Epoch 9: val_accuracy did not improve from 0.79545 Epoch 10: val_accuracy did not improve from 0.79545 Epoch 11: val_accuracy did not improve from 0.79545 Epoch 12: val_accuracy did not improve from 0.79545 Epoch 13: val_accuracy did not improve from 0.79545 Epoch 14: val_accuracy did not improve from 0.79545 Epoch 15: val_accuracy did not improve from 0.79545 Epoch 16: val_accuracy did not improve from 0.79545 Epoch 17: val_accuracy did not improve from 0.79545 Epoch 18: val_accuracy did not improve from 0.79545 Epoch 19: val_accuracy did not improve from 0.79545 Epoch 20: val_accuracy did not improve from 0.79545 Epoch 21: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.6885 - accuracy: 0.6136 - 37ms/epoch - 18ms/step ####################################################### the model mod94 use a learning rate = 1, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.88636 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.0298 - accuracy: 0.8182 - 43ms/epoch - 21ms/step ####################################################### the model mod95 use a learning rate = 2, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.56818 to 0.75000, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.81818 Epoch 6: val_accuracy did not improve from 0.81818 Epoch 7: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4361 - accuracy: 0.8409 - 48ms/epoch - 24ms/step ####################################################### the model mod96 use a learning rate = 3, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.47727 Epoch 4: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.54545 to 0.59091, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.61364 to 0.65909, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.65909 Epoch 11: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.77273 Epoch 16: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5 Epoch 17: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364 Epoch 25: val_accuracy did not improve from 0.86364 Epoch 26: val_accuracy did not improve from 0.86364 Epoch 27: val_accuracy did not improve from 0.86364 Epoch 28: val_accuracy did not improve from 0.86364 Epoch 29: val_accuracy did not improve from 0.86364 Epoch 30: val_accuracy did not improve from 0.86364 Epoch 31: val_accuracy did not improve from 0.86364 Epoch 32: val_accuracy did not improve from 0.86364 Epoch 33: val_accuracy did not improve from 0.86364 Epoch 34: val_accuracy did not improve from 0.86364 Epoch 35: val_accuracy did not improve from 0.86364 Epoch 36: val_accuracy did not improve from 0.86364 Epoch 37: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4527 - accuracy: 0.8409 - 33ms/epoch - 17ms/step ####################################################### the model mod97 use a learning rate = 4, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182 Epoch 28: val_accuracy did not improve from 0.93182 Epoch 29: val_accuracy did not improve from 0.93182 Epoch 30: val_accuracy did not improve from 0.93182 Epoch 31: val_accuracy did not improve from 0.93182 Epoch 32: val_accuracy did not improve from 0.93182 Epoch 33: val_accuracy did not improve from 0.93182 Epoch 34: val_accuracy did not improve from 0.93182 Epoch 35: val_accuracy did not improve from 0.93182 Epoch 36: val_accuracy did not improve from 0.93182 Epoch 37: val_accuracy did not improve from 0.93182 Epoch 38: val_accuracy did not improve from 0.93182 Epoch 39: val_accuracy did not improve from 0.93182 Epoch 40: val_accuracy did not improve from 0.93182 Epoch 41: val_accuracy did not improve from 0.93182 Epoch 42: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.3593 - accuracy: 0.8864 - 61ms/epoch - 31ms/step ####################################################### the model mod98 use a learning rate = 5, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.54545 Epoch 4: val_accuracy improved from 0.54545 to 0.59091, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.59091 Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.77273 Epoch 16: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.79545 Epoch 18: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.84091 Epoch 24: val_accuracy did not improve from 0.84091 Epoch 25: val_accuracy did not improve from 0.84091 Epoch 26: val_accuracy did not improve from 0.84091 Epoch 27: val_accuracy did not improve from 0.84091 Epoch 28: val_accuracy did not improve from 0.84091 Epoch 29: val_accuracy did not improve from 0.84091 Epoch 30: val_accuracy did not improve from 0.84091 Epoch 31: val_accuracy did not improve from 0.84091 Epoch 32: val_accuracy did not improve from 0.84091 Epoch 33: val_accuracy did not improve from 0.84091 Epoch 34: val_accuracy did not improve from 0.84091 Epoch 35: val_accuracy did not improve from 0.84091 Epoch 36: val_accuracy did not improve from 0.84091 Epoch 37: val_accuracy did not improve from 0.84091 Epoch 38: val_accuracy did not improve from 0.84091 Epoch 39: val_accuracy did not improve from 0.84091 Epoch 40: val_accuracy did not improve from 0.84091 Epoch 41: val_accuracy did not improve from 0.84091 Epoch 42: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3789 - accuracy: 0.8182 - 40ms/epoch - 20ms/step ####################################################### the model mod99 use a learning rate = 6, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.36364 Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364 Epoch 5: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.38636 Epoch 7: val_accuracy did not improve from 0.38636 Epoch 8: val_accuracy did not improve from 0.38636 Epoch 9: val_accuracy did not improve from 0.38636 Epoch 10: val_accuracy did not improve from 0.38636 Epoch 11: val_accuracy did not improve from 0.38636 Epoch 12: val_accuracy did not improve from 0.38636 Epoch 13: val_accuracy did not improve from 0.38636 Epoch 14: val_accuracy did not improve from 0.38636 Epoch 15: val_accuracy did not improve from 0.38636 Epoch 16: val_accuracy did not improve from 0.38636 Epoch 17: val_accuracy did not improve from 0.38636 Epoch 18: val_accuracy did not improve from 0.38636 Epoch 19: val_accuracy did not improve from 0.38636 Epoch 20: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.40909 Epoch 22: val_accuracy did not improve from 0.40909 Epoch 23: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.43182 Epoch 25: val_accuracy did not improve from 0.43182 Epoch 26: val_accuracy did not improve from 0.43182 Epoch 27: val_accuracy did not improve from 0.43182 Epoch 28: val_accuracy did not improve from 0.43182 Epoch 29: val_accuracy did not improve from 0.43182 Epoch 30: val_accuracy did not improve from 0.43182 Epoch 31: val_accuracy did not improve from 0.43182 Epoch 32: val_accuracy did not improve from 0.43182 Epoch 33: val_accuracy did not improve from 0.43182 Epoch 34: val_accuracy did not improve from 0.43182 Epoch 35: val_accuracy did not improve from 0.43182 Epoch 36: val_accuracy did not improve from 0.43182 Epoch 37: val_accuracy did not improve from 0.43182 Epoch 38: val_accuracy did not improve from 0.43182 Epoch 39: val_accuracy did not improve from 0.43182 Epoch 40: val_accuracy did not improve from 0.43182 Epoch 41: val_accuracy did not improve from 0.43182 Epoch 42: val_accuracy did not improve from 0.43182 Epoch 43: val_accuracy did not improve from 0.43182
2/2 - 0s - loss: 0.9472 - accuracy: 0.4318 - 34ms/epoch - 17ms/step ####################################################### the model mod100 use a learning rate = 7, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.59091 Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091 Epoch 5: val_accuracy did not improve from 0.59091 Epoch 6: val_accuracy did not improve from 0.59091 Epoch 7: val_accuracy did not improve from 0.59091 Epoch 8: val_accuracy did not improve from 0.59091 Epoch 9: val_accuracy did not improve from 0.59091 Epoch 10: val_accuracy did not improve from 0.59091 Epoch 11: val_accuracy did not improve from 0.59091 Epoch 12: val_accuracy did not improve from 0.59091 Epoch 13: val_accuracy did not improve from 0.59091 Epoch 14: val_accuracy did not improve from 0.59091 Epoch 15: val_accuracy did not improve from 0.59091 Epoch 16: val_accuracy did not improve from 0.59091 Epoch 17: val_accuracy did not improve from 0.59091 Epoch 18: val_accuracy did not improve from 0.59091 Epoch 19: val_accuracy did not improve from 0.59091 Epoch 20: val_accuracy did not improve from 0.59091 Epoch 21: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 0.6852 - accuracy: 0.5909 - 42ms/epoch - 21ms/step ####################################################### the model mod101 use a learning rate = 8, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.65909 Epoch 4: val_accuracy did not improve from 0.65909 Epoch 5: val_accuracy did not improve from 0.65909 Epoch 6: val_accuracy did not improve from 0.65909 Epoch 7: val_accuracy did not improve from 0.65909 Epoch 8: val_accuracy did not improve from 0.65909 Epoch 9: val_accuracy did not improve from 0.65909 Epoch 10: val_accuracy did not improve from 0.65909 Epoch 11: val_accuracy did not improve from 0.65909 Epoch 12: val_accuracy did not improve from 0.65909 Epoch 13: val_accuracy did not improve from 0.65909 Epoch 14: val_accuracy did not improve from 0.65909 Epoch 15: val_accuracy did not improve from 0.65909 Epoch 16: val_accuracy did not improve from 0.65909 Epoch 17: val_accuracy did not improve from 0.65909 Epoch 18: val_accuracy did not improve from 0.65909 Epoch 19: val_accuracy did not improve from 0.65909 Epoch 20: val_accuracy did not improve from 0.65909 Epoch 21: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.7369 - accuracy: 0.6591 - 34ms/epoch - 17ms/step ####################################################### the model mod102 use a learning rate = 9, l2 regularization = 3 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.75000 Epoch 3: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000 Epoch 5: val_accuracy did not improve from 0.75000 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy did not improve from 0.75000 Epoch 8: val_accuracy did not improve from 0.75000 Epoch 9: val_accuracy did not improve from 0.75000 Epoch 10: val_accuracy did not improve from 0.75000 Epoch 11: val_accuracy did not improve from 0.75000 Epoch 12: val_accuracy did not improve from 0.75000 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy did not improve from 0.75000 Epoch 15: val_accuracy did not improve from 0.75000 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.7096 - accuracy: 0.7500 - 47ms/epoch - 24ms/step ####################################################### the model mod103 use a learning rate = 0, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.81818 Epoch 3: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.8025 - accuracy: 0.7500 - 48ms/epoch - 24ms/step ####################################################### the model mod104 use a learning rate = 1, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3152 - accuracy: 0.9091 - 42ms/epoch - 21ms/step ####################################################### the model mod105 use a learning rate = 2, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000 Epoch 5: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.79545 Epoch 7: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.81818 Epoch 9: val_accuracy did not improve from 0.81818 Epoch 10: val_accuracy did not improve from 0.81818 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy did not improve from 0.81818 Epoch 13: val_accuracy did not improve from 0.81818 Epoch 14: val_accuracy did not improve from 0.81818 Epoch 15: val_accuracy did not improve from 0.81818 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy did not improve from 0.81818 Epoch 18: val_accuracy did not improve from 0.81818 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.5449 - accuracy: 0.7955 - 36ms/epoch - 18ms/step ####################################################### the model mod106 use a learning rate = 3, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.56818 Epoch 4: val_accuracy did not improve from 0.56818 Epoch 5: val_accuracy did not improve from 0.56818 Epoch 6: val_accuracy did not improve from 0.56818 Epoch 7: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.59091 Epoch 9: val_accuracy did not improve from 0.59091 Epoch 10: val_accuracy did not improve from 0.59091 Epoch 11: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.61364 Epoch 13: val_accuracy did not improve from 0.61364 Epoch 14: val_accuracy did not improve from 0.61364 Epoch 15: val_accuracy did not improve from 0.61364 Epoch 16: val_accuracy did not improve from 0.61364 Epoch 17: val_accuracy did not improve from 0.61364 Epoch 18: val_accuracy did not improve from 0.61364 Epoch 19: val_accuracy did not improve from 0.61364 Epoch 20: val_accuracy did not improve from 0.61364 Epoch 21: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.63636 Epoch 23: val_accuracy did not improve from 0.63636 Epoch 24: val_accuracy did not improve from 0.63636 Epoch 25: val_accuracy did not improve from 0.63636 Epoch 26: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5 Epoch 27: val_accuracy did not improve from 0.68182 Epoch 28: val_accuracy did not improve from 0.68182 Epoch 29: val_accuracy did not improve from 0.68182 Epoch 30: val_accuracy did not improve from 0.68182 Epoch 31: val_accuracy did not improve from 0.68182 Epoch 32: val_accuracy did not improve from 0.68182 Epoch 33: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 34: val_accuracy did not improve from 0.70455 Epoch 35: val_accuracy did not improve from 0.70455 Epoch 36: val_accuracy did not improve from 0.70455 Epoch 37: val_accuracy did not improve from 0.70455 Epoch 38: val_accuracy did not improve from 0.70455 Epoch 39: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 40: val_accuracy did not improve from 0.72727 Epoch 41: val_accuracy did not improve from 0.72727 Epoch 42: val_accuracy did not improve from 0.72727 Epoch 43: val_accuracy did not improve from 0.72727 Epoch 44: val_accuracy did not improve from 0.72727 Epoch 45: val_accuracy did not improve from 0.72727 Epoch 46: val_accuracy did not improve from 0.72727 Epoch 47: val_accuracy did not improve from 0.72727 Epoch 48: val_accuracy did not improve from 0.72727 Epoch 49: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 50: val_accuracy did not improve from 0.75000 Epoch 51: val_accuracy did not improve from 0.75000 Epoch 52: val_accuracy did not improve from 0.75000 Epoch 53: val_accuracy did not improve from 0.75000 Epoch 54: val_accuracy did not improve from 0.75000 Epoch 55: val_accuracy did not improve from 0.75000 Epoch 56: val_accuracy did not improve from 0.75000 Epoch 57: val_accuracy did not improve from 0.75000 Epoch 58: val_accuracy did not improve from 0.75000 Epoch 59: val_accuracy did not improve from 0.75000 Epoch 60: val_accuracy did not improve from 0.75000 Epoch 61: val_accuracy did not improve from 0.75000 Epoch 62: val_accuracy did not improve from 0.75000 Epoch 63: val_accuracy did not improve from 0.75000 Epoch 64: val_accuracy did not improve from 0.75000 Epoch 65: val_accuracy did not improve from 0.75000 Epoch 66: val_accuracy did not improve from 0.75000 Epoch 67: val_accuracy did not improve from 0.75000 Epoch 68: val_accuracy did not improve from 0.75000 Epoch 69: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.7531 - accuracy: 0.7500 - 42ms/epoch - 21ms/step ####################################################### the model mod107 use a learning rate = 4, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.61364 Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.63636 Epoch 6: val_accuracy did not improve from 0.63636 Epoch 7: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.68182 Epoch 10: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.70455 Epoch 12: val_accuracy did not improve from 0.70455 Epoch 13: val_accuracy did not improve from 0.70455 Epoch 14: val_accuracy did not improve from 0.70455 Epoch 15: val_accuracy did not improve from 0.70455 Epoch 16: val_accuracy did not improve from 0.70455 Epoch 17: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy did not improve from 0.72727 Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy did not improve from 0.75000 Epoch 25: val_accuracy did not improve from 0.75000 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy did not improve from 0.75000 Epoch 28: val_accuracy did not improve from 0.75000 Epoch 29: val_accuracy did not improve from 0.75000 Epoch 30: val_accuracy did not improve from 0.75000 Epoch 31: val_accuracy did not improve from 0.75000 Epoch 32: val_accuracy did not improve from 0.75000 Epoch 33: val_accuracy did not improve from 0.75000 Epoch 34: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.77273 Epoch 36: val_accuracy did not improve from 0.77273 Epoch 37: val_accuracy did not improve from 0.77273 Epoch 38: val_accuracy did not improve from 0.77273 Epoch 39: val_accuracy did not improve from 0.77273 Epoch 40: val_accuracy did not improve from 0.77273 Epoch 41: val_accuracy did not improve from 0.77273 Epoch 42: val_accuracy did not improve from 0.77273 Epoch 43: val_accuracy did not improve from 0.77273 Epoch 44: val_accuracy did not improve from 0.77273 Epoch 45: val_accuracy did not improve from 0.77273 Epoch 46: val_accuracy did not improve from 0.77273 Epoch 47: val_accuracy did not improve from 0.77273 Epoch 48: val_accuracy did not improve from 0.77273 Epoch 49: val_accuracy did not improve from 0.77273 Epoch 50: val_accuracy did not improve from 0.77273 Epoch 51: val_accuracy did not improve from 0.77273 Epoch 52: val_accuracy did not improve from 0.77273 Epoch 53: val_accuracy did not improve from 0.77273 Epoch 54: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5420 - accuracy: 0.7727 - 43ms/epoch - 21ms/step ####################################################### the model mod108 use a learning rate = 5, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.52273 Epoch 4: val_accuracy did not improve from 0.52273 Epoch 5: val_accuracy did not improve from 0.52273 Epoch 6: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.56818 Epoch 8: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.59091 Epoch 10: val_accuracy did not improve from 0.59091 Epoch 11: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.61364 Epoch 13: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.63636 Epoch 15: val_accuracy did not improve from 0.63636 Epoch 16: val_accuracy did not improve from 0.63636 Epoch 17: val_accuracy did not improve from 0.63636 Epoch 18: val_accuracy did not improve from 0.63636 Epoch 19: val_accuracy did not improve from 0.63636 Epoch 20: val_accuracy did not improve from 0.63636 Epoch 21: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 22: val_accuracy did not improve from 0.65909 Epoch 23: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 24: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.70455 Epoch 26: val_accuracy did not improve from 0.70455 Epoch 27: val_accuracy did not improve from 0.70455 Epoch 28: val_accuracy did not improve from 0.70455 Epoch 29: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 30: val_accuracy did not improve from 0.72727 Epoch 31: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.75000 Epoch 33: val_accuracy did not improve from 0.75000 Epoch 34: val_accuracy did not improve from 0.75000 Epoch 35: val_accuracy did not improve from 0.75000 Epoch 36: val_accuracy did not improve from 0.75000 Epoch 37: val_accuracy did not improve from 0.75000 Epoch 38: val_accuracy did not improve from 0.75000 Epoch 39: val_accuracy did not improve from 0.75000 Epoch 40: val_accuracy did not improve from 0.75000 Epoch 41: val_accuracy did not improve from 0.75000 Epoch 42: val_accuracy did not improve from 0.75000 Epoch 43: val_accuracy did not improve from 0.75000 Epoch 44: val_accuracy did not improve from 0.75000 Epoch 45: val_accuracy did not improve from 0.75000 Epoch 46: val_accuracy did not improve from 0.75000 Epoch 47: val_accuracy did not improve from 0.75000 Epoch 48: val_accuracy did not improve from 0.75000 Epoch 49: val_accuracy did not improve from 0.75000 Epoch 50: val_accuracy did not improve from 0.75000 Epoch 51: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.6408 - accuracy: 0.7500 - 38ms/epoch - 19ms/step ####################################################### the model mod109 use a learning rate = 6, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.38636 Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy did not improve from 0.38636 Epoch 7: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.40909 Epoch 9: val_accuracy did not improve from 0.40909 Epoch 10: val_accuracy did not improve from 0.40909 Epoch 11: val_accuracy did not improve from 0.40909 Epoch 12: val_accuracy did not improve from 0.40909 Epoch 13: val_accuracy did not improve from 0.40909 Epoch 14: val_accuracy did not improve from 0.40909 Epoch 15: val_accuracy did not improve from 0.40909 Epoch 16: val_accuracy did not improve from 0.40909 Epoch 17: val_accuracy did not improve from 0.40909 Epoch 18: val_accuracy did not improve from 0.40909 Epoch 19: val_accuracy did not improve from 0.40909 Epoch 20: val_accuracy did not improve from 0.40909 Epoch 21: val_accuracy did not improve from 0.40909 Epoch 22: val_accuracy did not improve from 0.40909 Epoch 23: val_accuracy did not improve from 0.40909 Epoch 24: val_accuracy did not improve from 0.40909 Epoch 25: val_accuracy did not improve from 0.40909 Epoch 26: val_accuracy did not improve from 0.40909 Epoch 27: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 0.9166 - accuracy: 0.4091 - 36ms/epoch - 18ms/step ####################################################### the model mod110 use a learning rate = 7, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.34091 Epoch 3: val_accuracy did not improve from 0.34091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.34091 Epoch 5: val_accuracy did not improve from 0.34091 Epoch 6: val_accuracy did not improve from 0.34091 Epoch 7: val_accuracy did not improve from 0.34091 Epoch 8: val_accuracy did not improve from 0.34091 Epoch 9: val_accuracy did not improve from 0.34091 Epoch 10: val_accuracy did not improve from 0.34091 Epoch 11: val_accuracy did not improve from 0.34091 Epoch 12: val_accuracy did not improve from 0.34091 Epoch 13: val_accuracy did not improve from 0.34091 Epoch 14: val_accuracy did not improve from 0.34091 Epoch 15: val_accuracy did not improve from 0.34091 Epoch 16: val_accuracy did not improve from 0.34091 Epoch 17: val_accuracy did not improve from 0.34091 Epoch 18: val_accuracy did not improve from 0.34091 Epoch 19: val_accuracy did not improve from 0.34091 Epoch 20: val_accuracy did not improve from 0.34091 Epoch 21: val_accuracy did not improve from 0.34091
2/2 - 0s - loss: 0.9174 - accuracy: 0.3409 - 39ms/epoch - 20ms/step ####################################################### the model mod111 use a learning rate = 8, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.45455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.45455 Epoch 4: val_accuracy did not improve from 0.45455 Epoch 5: val_accuracy did not improve from 0.45455 Epoch 6: val_accuracy did not improve from 0.45455 Epoch 7: val_accuracy did not improve from 0.45455 Epoch 8: val_accuracy did not improve from 0.45455 Epoch 9: val_accuracy did not improve from 0.45455 Epoch 10: val_accuracy did not improve from 0.45455 Epoch 11: val_accuracy did not improve from 0.45455 Epoch 12: val_accuracy did not improve from 0.45455 Epoch 13: val_accuracy did not improve from 0.45455 Epoch 14: val_accuracy did not improve from 0.45455 Epoch 15: val_accuracy did not improve from 0.45455 Epoch 16: val_accuracy did not improve from 0.45455 Epoch 17: val_accuracy did not improve from 0.45455 Epoch 18: val_accuracy did not improve from 0.45455 Epoch 19: val_accuracy did not improve from 0.45455 Epoch 20: val_accuracy did not improve from 0.45455 Epoch 21: val_accuracy did not improve from 0.45455
2/2 - 0s - loss: 0.8333 - accuracy: 0.4545 - 37ms/epoch - 18ms/step ####################################################### the model mod112 use a learning rate = 9, l2 regularization = 3 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.63636 Epoch 3: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.63636 Epoch 5: val_accuracy did not improve from 0.63636 Epoch 6: val_accuracy did not improve from 0.63636 Epoch 7: val_accuracy did not improve from 0.63636 Epoch 8: val_accuracy did not improve from 0.63636 Epoch 9: val_accuracy did not improve from 0.63636 Epoch 10: val_accuracy did not improve from 0.63636 Epoch 11: val_accuracy did not improve from 0.63636 Epoch 12: val_accuracy did not improve from 0.63636 Epoch 13: val_accuracy did not improve from 0.63636 Epoch 14: val_accuracy did not improve from 0.63636 Epoch 15: val_accuracy did not improve from 0.63636 Epoch 16: val_accuracy did not improve from 0.63636 Epoch 17: val_accuracy did not improve from 0.63636 Epoch 18: val_accuracy did not improve from 0.63636 Epoch 19: val_accuracy did not improve from 0.63636 Epoch 20: val_accuracy did not improve from 0.63636 Epoch 21: val_accuracy did not improve from 0.63636
2/2 - 0s - loss: 0.7298 - accuracy: 0.6364 - 37ms/epoch - 19ms/step ####################################################### the model mod113 use a learning rate = 0, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.59091 to 0.79545, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5716 - accuracy: 0.7955 - 43ms/epoch - 21ms/step ####################################################### the model mod114 use a learning rate = 1, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy did not improve from 0.84091 Epoch 17: val_accuracy did not improve from 0.84091 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091 Epoch 22: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.7465 - accuracy: 0.8182 - 36ms/epoch - 18ms/step ####################################################### the model mod115 use a learning rate = 2, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.86364 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6591 - accuracy: 0.8182 - 35ms/epoch - 17ms/step ####################################################### the model mod116 use a learning rate = 3, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.77273 Epoch 3: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364 Epoch 25: val_accuracy did not improve from 0.86364 Epoch 26: val_accuracy did not improve from 0.86364 Epoch 27: val_accuracy did not improve from 0.86364 Epoch 28: val_accuracy did not improve from 0.86364 Epoch 29: val_accuracy did not improve from 0.86364 Epoch 30: val_accuracy did not improve from 0.86364 Epoch 31: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5094 - accuracy: 0.8182 - 47ms/epoch - 23ms/step ####################################################### the model mod117 use a learning rate = 4, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5067 - accuracy: 0.7727 - 53ms/epoch - 27ms/step ####################################################### the model mod118 use a learning rate = 5, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.88636 Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5352 - accuracy: 0.8409 - 46ms/epoch - 23ms/step ####################################################### the model mod119 use a learning rate = 6, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5192 - accuracy: 0.8864 - 50ms/epoch - 25ms/step ####################################################### the model mod120 use a learning rate = 7, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5476 - accuracy: 0.8636 - 34ms/epoch - 17ms/step ####################################################### the model mod121 use a learning rate = 8, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.77273 Epoch 3: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.6529 - accuracy: 0.7500 - 41ms/epoch - 20ms/step ####################################################### the model mod122 use a learning rate = 9, l2 regularization = 3 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.68182 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6571 - accuracy: 0.7955 - 50ms/epoch - 25ms/step ####################################################### the model mod123 use a learning rate = 0, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.50000 to 0.79545, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.79545 Epoch 5: val_accuracy did not improve from 0.79545 Epoch 6: val_accuracy did not improve from 0.79545 Epoch 7: val_accuracy did not improve from 0.79545 Epoch 8: val_accuracy did not improve from 0.79545 Epoch 9: val_accuracy did not improve from 0.79545 Epoch 10: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy did not improve from 0.81818 Epoch 13: val_accuracy did not improve from 0.81818 Epoch 14: val_accuracy did not improve from 0.81818 Epoch 15: val_accuracy did not improve from 0.81818 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy did not improve from 0.81818 Epoch 18: val_accuracy did not improve from 0.81818 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.8507 - accuracy: 0.6818 - 41ms/epoch - 21ms/step ####################################################### the model mod124 use a learning rate = 1, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.86364 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 1.1400 - accuracy: 0.7955 - 38ms/epoch - 19ms/step ####################################################### the model mod125 use a learning rate = 2, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182 Epoch 28: val_accuracy did not improve from 0.93182 Epoch 29: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4282 - accuracy: 0.8636 - 36ms/epoch - 18ms/step ####################################################### the model mod126 use a learning rate = 3, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.43182 to 0.47727, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.47727 to 0.59091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.61364 to 0.70455, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.77273 Epoch 10: val_accuracy did not improve from 0.77273 Epoch 11: val_accuracy did not improve from 0.77273 Epoch 12: val_accuracy did not improve from 0.77273 Epoch 13: val_accuracy did not improve from 0.77273 Epoch 14: val_accuracy did not improve from 0.77273 Epoch 15: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 16: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.81818 Epoch 18: val_accuracy did not improve from 0.81818 Epoch 19: val_accuracy did not improve from 0.81818 Epoch 20: val_accuracy did not improve from 0.81818 Epoch 21: val_accuracy did not improve from 0.81818 Epoch 22: val_accuracy did not improve from 0.81818 Epoch 23: val_accuracy did not improve from 0.81818 Epoch 24: val_accuracy did not improve from 0.81818 Epoch 25: val_accuracy did not improve from 0.81818 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818 Epoch 31: val_accuracy did not improve from 0.81818 Epoch 32: val_accuracy did not improve from 0.81818 Epoch 33: val_accuracy did not improve from 0.81818 Epoch 34: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.86364 Epoch 36: val_accuracy did not improve from 0.86364 Epoch 37: val_accuracy did not improve from 0.86364 Epoch 38: val_accuracy did not improve from 0.86364 Epoch 39: val_accuracy did not improve from 0.86364 Epoch 40: val_accuracy did not improve from 0.86364 Epoch 41: val_accuracy did not improve from 0.86364 Epoch 42: val_accuracy did not improve from 0.86364 Epoch 43: val_accuracy did not improve from 0.86364 Epoch 44: val_accuracy did not improve from 0.86364 Epoch 45: val_accuracy did not improve from 0.86364 Epoch 46: val_accuracy did not improve from 0.86364 Epoch 47: val_accuracy did not improve from 0.86364 Epoch 48: val_accuracy did not improve from 0.86364 Epoch 49: val_accuracy did not improve from 0.86364 Epoch 50: val_accuracy did not improve from 0.86364 Epoch 51: val_accuracy did not improve from 0.86364 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.3502 - accuracy: 0.8636 - 55ms/epoch - 27ms/step ####################################################### the model mod127 use a learning rate = 4, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.25000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.25000 to 0.34091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.34091 to 0.43182, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.43182 to 0.47727, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.47727 to 0.68182, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3542 - accuracy: 0.8864 - 33ms/epoch - 16ms/step ####################################################### the model mod128 use a learning rate = 5, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.29545 to 0.38636, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.38636 to 0.47727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.47727 to 0.56818, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.72727 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy did not improve from 0.72727 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 18: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.79545 Epoch 20: val_accuracy did not improve from 0.79545 Epoch 21: val_accuracy did not improve from 0.79545 Epoch 22: val_accuracy did not improve from 0.79545 Epoch 23: val_accuracy did not improve from 0.79545 Epoch 24: val_accuracy did not improve from 0.79545 Epoch 25: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 26: val_accuracy did not improve from 0.81818 Epoch 27: val_accuracy did not improve from 0.81818 Epoch 28: val_accuracy did not improve from 0.81818 Epoch 29: val_accuracy did not improve from 0.81818 Epoch 30: val_accuracy did not improve from 0.81818 Epoch 31: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.84091 Epoch 33: val_accuracy did not improve from 0.84091 Epoch 34: val_accuracy did not improve from 0.84091 Epoch 35: val_accuracy did not improve from 0.84091 Epoch 36: val_accuracy did not improve from 0.84091 Epoch 37: val_accuracy did not improve from 0.84091 Epoch 38: val_accuracy did not improve from 0.84091 Epoch 39: val_accuracy did not improve from 0.84091 Epoch 40: val_accuracy did not improve from 0.84091 Epoch 41: val_accuracy did not improve from 0.84091 Epoch 42: val_accuracy did not improve from 0.84091 Epoch 43: val_accuracy did not improve from 0.84091 Epoch 44: val_accuracy did not improve from 0.84091 Epoch 45: val_accuracy did not improve from 0.84091 Epoch 46: val_accuracy did not improve from 0.84091 Epoch 47: val_accuracy did not improve from 0.84091 Epoch 48: val_accuracy did not improve from 0.84091 Epoch 49: val_accuracy did not improve from 0.84091 Epoch 50: val_accuracy did not improve from 0.84091 Epoch 51: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3362 - accuracy: 0.8409 - 34ms/epoch - 17ms/step ####################################################### the model mod129 use a learning rate = 6, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.27273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.27273 Epoch 3: val_accuracy did not improve from 0.27273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.27273 Epoch 5: val_accuracy did not improve from 0.27273 Epoch 6: val_accuracy did not improve from 0.27273 Epoch 7: val_accuracy did not improve from 0.27273 Epoch 8: val_accuracy did not improve from 0.27273 Epoch 9: val_accuracy did not improve from 0.27273 Epoch 10: val_accuracy did not improve from 0.27273 Epoch 11: val_accuracy did not improve from 0.27273 Epoch 12: val_accuracy did not improve from 0.27273 Epoch 13: val_accuracy did not improve from 0.27273 Epoch 14: val_accuracy did not improve from 0.27273 Epoch 15: val_accuracy did not improve from 0.27273 Epoch 16: val_accuracy did not improve from 0.27273 Epoch 17: val_accuracy did not improve from 0.27273 Epoch 18: val_accuracy improved from 0.27273 to 0.29545, saving model to best_model.h5 Epoch 19: val_accuracy did not improve from 0.29545 Epoch 20: val_accuracy did not improve from 0.29545 Epoch 21: val_accuracy did not improve from 0.29545 Epoch 22: val_accuracy did not improve from 0.29545 Epoch 23: val_accuracy did not improve from 0.29545 Epoch 24: val_accuracy did not improve from 0.29545 Epoch 25: val_accuracy did not improve from 0.29545 Epoch 26: val_accuracy did not improve from 0.29545 Epoch 27: val_accuracy did not improve from 0.29545 Epoch 28: val_accuracy did not improve from 0.29545 Epoch 29: val_accuracy did not improve from 0.29545 Epoch 30: val_accuracy did not improve from 0.29545 Epoch 31: val_accuracy improved from 0.29545 to 0.31818, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.31818 Epoch 33: val_accuracy did not improve from 0.31818 Epoch 34: val_accuracy did not improve from 0.31818 Epoch 35: val_accuracy did not improve from 0.31818 Epoch 36: val_accuracy did not improve from 0.31818 Epoch 37: val_accuracy did not improve from 0.31818 Epoch 38: val_accuracy improved from 0.31818 to 0.34091, saving model to best_model.h5 Epoch 39: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5 Epoch 40: val_accuracy did not improve from 0.36364 Epoch 41: val_accuracy did not improve from 0.36364 Epoch 42: val_accuracy did not improve from 0.36364 Epoch 43: val_accuracy did not improve from 0.36364 Epoch 44: val_accuracy did not improve from 0.36364 Epoch 45: val_accuracy did not improve from 0.36364 Epoch 46: val_accuracy did not improve from 0.36364 Epoch 47: val_accuracy did not improve from 0.36364 Epoch 48: val_accuracy did not improve from 0.36364 Epoch 49: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5 Epoch 50: val_accuracy did not improve from 0.38636 Epoch 51: val_accuracy did not improve from 0.38636 Epoch 52: val_accuracy did not improve from 0.38636 Epoch 53: val_accuracy did not improve from 0.38636 Epoch 54: val_accuracy did not improve from 0.38636 Epoch 55: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 56: val_accuracy did not improve from 0.40909 Epoch 57: val_accuracy did not improve from 0.40909 Epoch 58: val_accuracy did not improve from 0.40909 Epoch 59: val_accuracy did not improve from 0.40909 Epoch 60: val_accuracy did not improve from 0.40909 Epoch 61: val_accuracy did not improve from 0.40909 Epoch 62: val_accuracy did not improve from 0.40909 Epoch 63: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 64: val_accuracy did not improve from 0.43182 Epoch 65: val_accuracy did not improve from 0.43182 Epoch 66: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 67: val_accuracy did not improve from 0.45455 Epoch 68: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 69: val_accuracy did not improve from 0.47727 Epoch 70: val_accuracy did not improve from 0.47727 Epoch 71: val_accuracy did not improve from 0.47727 Epoch 72: val_accuracy did not improve from 0.47727 Epoch 73: val_accuracy did not improve from 0.47727 Epoch 74: val_accuracy did not improve from 0.47727 Epoch 75: val_accuracy did not improve from 0.47727 Epoch 76: val_accuracy did not improve from 0.47727 Epoch 77: val_accuracy did not improve from 0.47727 Epoch 78: val_accuracy did not improve from 0.47727 Epoch 79: val_accuracy did not improve from 0.47727 Epoch 80: val_accuracy did not improve from 0.47727 Epoch 81: val_accuracy did not improve from 0.47727 Epoch 82: val_accuracy did not improve from 0.47727 Epoch 83: val_accuracy did not improve from 0.47727 Epoch 84: val_accuracy did not improve from 0.47727 Epoch 85: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 86: val_accuracy did not improve from 0.50000 Epoch 87: val_accuracy did not improve from 0.50000 Epoch 88: val_accuracy did not improve from 0.50000 Epoch 89: val_accuracy did not improve from 0.50000 Epoch 90: val_accuracy did not improve from 0.50000 Epoch 91: val_accuracy did not improve from 0.50000 Epoch 92: val_accuracy did not improve from 0.50000 Epoch 93: val_accuracy did not improve from 0.50000 Epoch 94: val_accuracy did not improve from 0.50000 Epoch 95: val_accuracy did not improve from 0.50000 Epoch 96: val_accuracy did not improve from 0.50000 Epoch 97: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 98: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5 Epoch 99: val_accuracy did not improve from 0.54545 Epoch 100: val_accuracy did not improve from 0.54545 Epoch 101: val_accuracy did not improve from 0.54545 Epoch 102: val_accuracy did not improve from 0.54545 Epoch 103: val_accuracy did not improve from 0.54545 Epoch 104: val_accuracy did not improve from 0.54545 Epoch 105: val_accuracy did not improve from 0.54545 Epoch 106: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 107: val_accuracy did not improve from 0.56818 Epoch 108: val_accuracy did not improve from 0.56818 Epoch 109: val_accuracy did not improve from 0.56818 Epoch 110: val_accuracy did not improve from 0.56818 Epoch 111: val_accuracy did not improve from 0.56818 Epoch 112: val_accuracy did not improve from 0.56818 Epoch 113: val_accuracy did not improve from 0.56818 Epoch 114: val_accuracy did not improve from 0.56818 Epoch 115: val_accuracy did not improve from 0.56818 Epoch 116: val_accuracy did not improve from 0.56818 Epoch 117: val_accuracy did not improve from 0.56818 Epoch 118: val_accuracy did not improve from 0.56818 Epoch 119: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 120: val_accuracy did not improve from 0.59091 Epoch 121: val_accuracy did not improve from 0.59091 Epoch 122: val_accuracy did not improve from 0.59091 Epoch 123: val_accuracy did not improve from 0.59091 Epoch 124: val_accuracy did not improve from 0.59091 Epoch 125: val_accuracy did not improve from 0.59091 Epoch 126: val_accuracy did not improve from 0.59091 Epoch 127: val_accuracy did not improve from 0.59091 Epoch 128: val_accuracy did not improve from 0.59091 Epoch 129: val_accuracy did not improve from 0.59091 Epoch 130: val_accuracy did not improve from 0.59091 Epoch 131: val_accuracy did not improve from 0.59091 Epoch 132: val_accuracy did not improve from 0.59091 Epoch 133: val_accuracy did not improve from 0.59091 Epoch 134: val_accuracy did not improve from 0.59091 Epoch 135: val_accuracy did not improve from 0.59091 Epoch 136: val_accuracy did not improve from 0.59091 Epoch 137: val_accuracy did not improve from 0.59091 Epoch 138: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 139: val_accuracy did not improve from 0.61364 Epoch 140: val_accuracy did not improve from 0.61364 Epoch 141: val_accuracy did not improve from 0.61364 Epoch 142: val_accuracy did not improve from 0.61364 Epoch 143: val_accuracy did not improve from 0.61364 Epoch 144: val_accuracy did not improve from 0.61364 Epoch 145: val_accuracy did not improve from 0.61364 Epoch 146: val_accuracy did not improve from 0.61364 Epoch 147: val_accuracy did not improve from 0.61364 Epoch 148: val_accuracy did not improve from 0.61364 Epoch 149: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 150: val_accuracy did not improve from 0.63636 Epoch 151: val_accuracy did not improve from 0.63636 Epoch 152: val_accuracy did not improve from 0.63636 Epoch 153: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 154: val_accuracy did not improve from 0.65909 Epoch 155: val_accuracy did not improve from 0.65909 Epoch 156: val_accuracy did not improve from 0.65909 Epoch 157: val_accuracy did not improve from 0.65909 Epoch 158: val_accuracy did not improve from 0.65909 Epoch 159: val_accuracy did not improve from 0.65909 Epoch 160: val_accuracy did not improve from 0.65909 Epoch 161: val_accuracy did not improve from 0.65909 Epoch 162: val_accuracy did not improve from 0.65909 Epoch 163: val_accuracy did not improve from 0.65909 Epoch 164: val_accuracy did not improve from 0.65909 Epoch 165: val_accuracy did not improve from 0.65909 Epoch 166: val_accuracy did not improve from 0.65909 Epoch 167: val_accuracy did not improve from 0.65909 Epoch 168: val_accuracy did not improve from 0.65909 Epoch 169: val_accuracy did not improve from 0.65909 Epoch 170: val_accuracy did not improve from 0.65909 Epoch 171: val_accuracy did not improve from 0.65909 Epoch 172: val_accuracy did not improve from 0.65909 Epoch 173: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.6390 - accuracy: 0.6591 - 38ms/epoch - 19ms/step ####################################################### the model mod130 use a learning rate = 7, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.70455 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy did not improve from 0.70455 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy did not improve from 0.70455 Epoch 10: val_accuracy did not improve from 0.70455 Epoch 11: val_accuracy did not improve from 0.70455 Epoch 12: val_accuracy did not improve from 0.70455 Epoch 13: val_accuracy did not improve from 0.70455 Epoch 14: val_accuracy did not improve from 0.70455 Epoch 15: val_accuracy did not improve from 0.70455 Epoch 16: val_accuracy did not improve from 0.70455 Epoch 17: val_accuracy did not improve from 0.70455 Epoch 18: val_accuracy did not improve from 0.70455 Epoch 19: val_accuracy did not improve from 0.70455 Epoch 20: val_accuracy did not improve from 0.70455 Epoch 21: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 0.7560 - accuracy: 0.7045 - 34ms/epoch - 17ms/step ####################################################### the model mod131 use a learning rate = 8, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.65909 Epoch 3: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.65909 Epoch 5: val_accuracy did not improve from 0.65909 Epoch 6: val_accuracy did not improve from 0.65909 Epoch 7: val_accuracy did not improve from 0.65909 Epoch 8: val_accuracy did not improve from 0.65909 Epoch 9: val_accuracy did not improve from 0.65909 Epoch 10: val_accuracy did not improve from 0.65909 Epoch 11: val_accuracy did not improve from 0.65909 Epoch 12: val_accuracy did not improve from 0.65909 Epoch 13: val_accuracy did not improve from 0.65909 Epoch 14: val_accuracy did not improve from 0.65909 Epoch 15: val_accuracy did not improve from 0.65909 Epoch 16: val_accuracy did not improve from 0.65909 Epoch 17: val_accuracy did not improve from 0.65909 Epoch 18: val_accuracy did not improve from 0.65909 Epoch 19: val_accuracy did not improve from 0.65909 Epoch 20: val_accuracy did not improve from 0.65909 Epoch 21: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.6901 - accuracy: 0.6591 - 74ms/epoch - 37ms/step ####################################################### the model mod132 use a learning rate = 9, l2 regularization = 4 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.29545 Epoch 3: val_accuracy did not improve from 0.29545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.29545 Epoch 5: val_accuracy did not improve from 0.29545 Epoch 6: val_accuracy did not improve from 0.29545 Epoch 7: val_accuracy did not improve from 0.29545 Epoch 8: val_accuracy did not improve from 0.29545 Epoch 9: val_accuracy did not improve from 0.29545 Epoch 10: val_accuracy did not improve from 0.29545 Epoch 11: val_accuracy did not improve from 0.29545 Epoch 12: val_accuracy did not improve from 0.29545 Epoch 13: val_accuracy did not improve from 0.29545 Epoch 14: val_accuracy did not improve from 0.29545 Epoch 15: val_accuracy did not improve from 0.29545 Epoch 16: val_accuracy did not improve from 0.29545 Epoch 17: val_accuracy did not improve from 0.29545 Epoch 18: val_accuracy did not improve from 0.29545 Epoch 19: val_accuracy did not improve from 0.29545 Epoch 20: val_accuracy did not improve from 0.29545 Epoch 21: val_accuracy did not improve from 0.29545
2/2 - 0s - loss: 1.0313 - accuracy: 0.2955 - 35ms/epoch - 17ms/step ####################################################### the model mod133 use a learning rate = 0, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.88636 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.0391 - accuracy: 0.7273 - 36ms/epoch - 18ms/step ####################################################### the model mod134 use a learning rate = 1, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4260 - accuracy: 0.7955 - 44ms/epoch - 22ms/step ####################################################### the model mod135 use a learning rate = 2, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.54545 Epoch 4: val_accuracy did not improve from 0.54545 Epoch 5: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.56818 Epoch 7: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 8: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.63636 Epoch 10: val_accuracy improved from 0.63636 to 0.70455, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.75000 Epoch 14: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 15: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.81818 Epoch 17: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091 Epoch 22: val_accuracy did not improve from 0.84091 Epoch 23: val_accuracy did not improve from 0.84091 Epoch 24: val_accuracy did not improve from 0.84091 Epoch 25: val_accuracy did not improve from 0.84091 Epoch 26: val_accuracy did not improve from 0.84091 Epoch 27: val_accuracy did not improve from 0.84091 Epoch 28: val_accuracy did not improve from 0.84091 Epoch 29: val_accuracy did not improve from 0.84091 Epoch 30: val_accuracy did not improve from 0.84091 Epoch 31: val_accuracy did not improve from 0.84091 Epoch 32: val_accuracy did not improve from 0.84091 Epoch 33: val_accuracy did not improve from 0.84091 Epoch 34: val_accuracy did not improve from 0.84091 Epoch 35: val_accuracy did not improve from 0.84091 Epoch 36: val_accuracy did not improve from 0.84091 Epoch 37: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3855 - accuracy: 0.8409 - 41ms/epoch - 20ms/step ####################################################### the model mod136 use a learning rate = 3, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.38636 Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy did not improve from 0.38636 Epoch 7: val_accuracy did not improve from 0.38636 Epoch 8: val_accuracy did not improve from 0.38636 Epoch 9: val_accuracy did not improve from 0.38636 Epoch 10: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.40909 Epoch 12: val_accuracy did not improve from 0.40909 Epoch 13: val_accuracy did not improve from 0.40909 Epoch 14: val_accuracy did not improve from 0.40909 Epoch 15: val_accuracy did not improve from 0.40909 Epoch 16: val_accuracy did not improve from 0.40909 Epoch 17: val_accuracy did not improve from 0.40909 Epoch 18: val_accuracy did not improve from 0.40909 Epoch 19: val_accuracy did not improve from 0.40909 Epoch 20: val_accuracy did not improve from 0.40909 Epoch 21: val_accuracy did not improve from 0.40909 Epoch 22: val_accuracy did not improve from 0.40909 Epoch 23: val_accuracy did not improve from 0.40909 Epoch 24: val_accuracy did not improve from 0.40909 Epoch 25: val_accuracy did not improve from 0.40909 Epoch 26: val_accuracy did not improve from 0.40909 Epoch 27: val_accuracy did not improve from 0.40909 Epoch 28: val_accuracy did not improve from 0.40909 Epoch 29: val_accuracy did not improve from 0.40909 Epoch 30: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 0.9324 - accuracy: 0.4091 - 37ms/epoch - 18ms/step ####################################################### the model mod137 use a learning rate = 4, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455 Epoch 5: val_accuracy did not improve from 0.70455 Epoch 6: val_accuracy did not improve from 0.70455 Epoch 7: val_accuracy did not improve from 0.70455 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.72727 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy did not improve from 0.72727 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy did not improve from 0.75000 Epoch 25: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 30: val_accuracy did not improve from 0.79545 Epoch 31: val_accuracy did not improve from 0.79545 Epoch 32: val_accuracy did not improve from 0.79545 Epoch 33: val_accuracy did not improve from 0.79545 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 38: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.84091 Epoch 40: val_accuracy did not improve from 0.84091 Epoch 41: val_accuracy did not improve from 0.84091 Epoch 42: val_accuracy did not improve from 0.84091 Epoch 43: val_accuracy did not improve from 0.84091 Epoch 44: val_accuracy did not improve from 0.84091 Epoch 45: val_accuracy did not improve from 0.84091 Epoch 46: val_accuracy did not improve from 0.84091 Epoch 47: val_accuracy did not improve from 0.84091 Epoch 48: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 49: val_accuracy did not improve from 0.86364 Epoch 50: val_accuracy did not improve from 0.86364 Epoch 51: val_accuracy did not improve from 0.86364 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364 Epoch 55: val_accuracy did not improve from 0.86364 Epoch 56: val_accuracy did not improve from 0.86364 Epoch 57: val_accuracy did not improve from 0.86364 Epoch 58: val_accuracy did not improve from 0.86364 Epoch 59: val_accuracy did not improve from 0.86364 Epoch 60: val_accuracy did not improve from 0.86364 Epoch 61: val_accuracy did not improve from 0.86364 Epoch 62: val_accuracy did not improve from 0.86364 Epoch 63: val_accuracy did not improve from 0.86364 Epoch 64: val_accuracy did not improve from 0.86364 Epoch 65: val_accuracy did not improve from 0.86364 Epoch 66: val_accuracy did not improve from 0.86364 Epoch 67: val_accuracy did not improve from 0.86364 Epoch 68: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4758 - accuracy: 0.8636 - 35ms/epoch - 17ms/step ####################################################### the model mod138 use a learning rate = 5, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.34091 Epoch 3: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.40909 Epoch 7: val_accuracy did not improve from 0.40909 Epoch 8: val_accuracy did not improve from 0.40909 Epoch 9: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 12: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.50000 Epoch 14: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.52273 Epoch 16: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5 Epoch 17: val_accuracy did not improve from 0.54545 Epoch 18: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 19: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 20: val_accuracy did not improve from 0.59091 Epoch 21: val_accuracy did not improve from 0.59091 Epoch 22: val_accuracy did not improve from 0.59091 Epoch 23: val_accuracy did not improve from 0.59091 Epoch 24: val_accuracy did not improve from 0.59091 Epoch 25: val_accuracy did not improve from 0.59091 Epoch 26: val_accuracy did not improve from 0.59091 Epoch 27: val_accuracy did not improve from 0.59091 Epoch 28: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5 Epoch 29: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 30: val_accuracy did not improve from 0.63636 Epoch 31: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 32: val_accuracy did not improve from 0.65909 Epoch 33: val_accuracy did not improve from 0.65909 Epoch 34: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.68182 Epoch 36: val_accuracy did not improve from 0.68182 Epoch 37: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 38: val_accuracy did not improve from 0.70455 Epoch 39: val_accuracy did not improve from 0.70455 Epoch 40: val_accuracy did not improve from 0.70455 Epoch 41: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 42: val_accuracy did not improve from 0.72727 Epoch 43: val_accuracy did not improve from 0.72727 Epoch 44: val_accuracy did not improve from 0.72727 Epoch 45: val_accuracy did not improve from 0.72727 Epoch 46: val_accuracy did not improve from 0.72727 Epoch 47: val_accuracy did not improve from 0.72727 Epoch 48: val_accuracy did not improve from 0.72727 Epoch 49: val_accuracy did not improve from 0.72727 Epoch 50: val_accuracy did not improve from 0.72727 Epoch 51: val_accuracy did not improve from 0.72727 Epoch 52: val_accuracy did not improve from 0.72727 Epoch 53: val_accuracy did not improve from 0.72727 Epoch 54: val_accuracy did not improve from 0.72727 Epoch 55: val_accuracy did not improve from 0.72727 Epoch 56: val_accuracy did not improve from 0.72727 Epoch 57: val_accuracy did not improve from 0.72727 Epoch 58: val_accuracy did not improve from 0.72727 Epoch 59: val_accuracy did not improve from 0.72727 Epoch 60: val_accuracy did not improve from 0.72727 Epoch 61: val_accuracy did not improve from 0.72727
2/2 - 0s - loss: 0.7003 - accuracy: 0.7273 - 55ms/epoch - 28ms/step ####################################################### the model mod139 use a learning rate = 6, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.47727 Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727 Epoch 5: val_accuracy did not improve from 0.47727 Epoch 6: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.50000 Epoch 8: val_accuracy did not improve from 0.50000 Epoch 9: val_accuracy did not improve from 0.50000 Epoch 10: val_accuracy did not improve from 0.50000 Epoch 11: val_accuracy did not improve from 0.50000 Epoch 12: val_accuracy did not improve from 0.50000 Epoch 13: val_accuracy did not improve from 0.50000 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy did not improve from 0.50000 Epoch 16: val_accuracy did not improve from 0.50000 Epoch 17: val_accuracy did not improve from 0.50000 Epoch 18: val_accuracy did not improve from 0.50000 Epoch 19: val_accuracy did not improve from 0.50000 Epoch 20: val_accuracy did not improve from 0.50000 Epoch 21: val_accuracy did not improve from 0.50000 Epoch 22: val_accuracy did not improve from 0.50000 Epoch 23: val_accuracy did not improve from 0.50000 Epoch 24: val_accuracy did not improve from 0.50000 Epoch 25: val_accuracy did not improve from 0.50000 Epoch 26: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 0.9501 - accuracy: 0.5000 - 50ms/epoch - 25ms/step ####################################################### the model mod140 use a learning rate = 7, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.36364 Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364 Epoch 5: val_accuracy did not improve from 0.36364 Epoch 6: val_accuracy did not improve from 0.36364 Epoch 7: val_accuracy did not improve from 0.36364 Epoch 8: val_accuracy did not improve from 0.36364 Epoch 9: val_accuracy did not improve from 0.36364 Epoch 10: val_accuracy did not improve from 0.36364 Epoch 11: val_accuracy did not improve from 0.36364 Epoch 12: val_accuracy did not improve from 0.36364 Epoch 13: val_accuracy did not improve from 0.36364 Epoch 14: val_accuracy did not improve from 0.36364 Epoch 15: val_accuracy did not improve from 0.36364 Epoch 16: val_accuracy did not improve from 0.36364 Epoch 17: val_accuracy did not improve from 0.36364 Epoch 18: val_accuracy did not improve from 0.36364 Epoch 19: val_accuracy did not improve from 0.36364 Epoch 20: val_accuracy did not improve from 0.36364 Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.8779 - accuracy: 0.3636 - 37ms/epoch - 19ms/step ####################################################### the model mod141 use a learning rate = 8, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.52273 Epoch 4: val_accuracy did not improve from 0.52273 Epoch 5: val_accuracy did not improve from 0.52273 Epoch 6: val_accuracy did not improve from 0.52273 Epoch 7: val_accuracy did not improve from 0.52273 Epoch 8: val_accuracy did not improve from 0.52273 Epoch 9: val_accuracy did not improve from 0.52273 Epoch 10: val_accuracy did not improve from 0.52273 Epoch 11: val_accuracy did not improve from 0.52273 Epoch 12: val_accuracy did not improve from 0.52273 Epoch 13: val_accuracy did not improve from 0.52273 Epoch 14: val_accuracy did not improve from 0.52273 Epoch 15: val_accuracy did not improve from 0.52273 Epoch 16: val_accuracy did not improve from 0.52273 Epoch 17: val_accuracy did not improve from 0.52273 Epoch 18: val_accuracy did not improve from 0.52273 Epoch 19: val_accuracy did not improve from 0.52273 Epoch 20: val_accuracy did not improve from 0.52273 Epoch 21: val_accuracy did not improve from 0.52273
2/2 - 0s - loss: 0.9006 - accuracy: 0.5227 - 36ms/epoch - 18ms/step ####################################################### the model mod142 use a learning rate = 9, l2 regularization = 4 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.36364 Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364 Epoch 5: val_accuracy did not improve from 0.36364 Epoch 6: val_accuracy did not improve from 0.36364 Epoch 7: val_accuracy did not improve from 0.36364 Epoch 8: val_accuracy did not improve from 0.36364 Epoch 9: val_accuracy did not improve from 0.36364 Epoch 10: val_accuracy did not improve from 0.36364 Epoch 11: val_accuracy did not improve from 0.36364 Epoch 12: val_accuracy did not improve from 0.36364 Epoch 13: val_accuracy did not improve from 0.36364 Epoch 14: val_accuracy did not improve from 0.36364 Epoch 15: val_accuracy did not improve from 0.36364 Epoch 16: val_accuracy did not improve from 0.36364 Epoch 17: val_accuracy did not improve from 0.36364 Epoch 18: val_accuracy did not improve from 0.36364 Epoch 19: val_accuracy did not improve from 0.36364 Epoch 20: val_accuracy did not improve from 0.36364 Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.9550 - accuracy: 0.3636 - 36ms/epoch - 18ms/step ####################################################### the model mod143 use a learning rate = 0, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.84091 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy did not improve from 0.84091 Epoch 7: val_accuracy did not improve from 0.84091 Epoch 8: val_accuracy did not improve from 0.84091 Epoch 9: val_accuracy did not improve from 0.84091 Epoch 10: val_accuracy did not improve from 0.84091 Epoch 11: val_accuracy did not improve from 0.84091 Epoch 12: val_accuracy did not improve from 0.84091 Epoch 13: val_accuracy did not improve from 0.84091 Epoch 14: val_accuracy did not improve from 0.84091 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy did not improve from 0.84091 Epoch 17: val_accuracy did not improve from 0.84091 Epoch 18: val_accuracy did not improve from 0.84091 Epoch 19: val_accuracy did not improve from 0.84091 Epoch 20: val_accuracy did not improve from 0.84091 Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.6591 - accuracy: 0.7727 - 34ms/epoch - 17ms/step ####################################################### the model mod144 use a learning rate = 1, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545 Epoch 4: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.6948 - accuracy: 0.8182 - 34ms/epoch - 17ms/step ####################################################### the model mod145 use a learning rate = 2, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy did not improve from 0.90909 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7669 - accuracy: 0.7727 - 34ms/epoch - 17ms/step ####################################################### the model mod146 use a learning rate = 3, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6180 - accuracy: 0.7955 - 35ms/epoch - 17ms/step ####################################################### the model mod147 use a learning rate = 4, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.90909, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909 Epoch 5: val_accuracy did not improve from 0.90909 Epoch 6: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5641 - accuracy: 0.8636 - 48ms/epoch - 24ms/step ####################################################### the model mod148 use a learning rate = 5, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5773 - accuracy: 0.8182 - 39ms/epoch - 19ms/step ####################################################### the model mod149 use a learning rate = 6, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4497 - accuracy: 0.8409 - 40ms/epoch - 20ms/step ####################################################### the model mod150 use a learning rate = 7, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6559 - accuracy: 0.7955 - 34ms/epoch - 17ms/step ####################################################### the model mod151 use a learning rate = 8, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545 Epoch 4: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5038 - accuracy: 0.8182 - 34ms/epoch - 17ms/step ####################################################### the model mod152 use a learning rate = 9, l2 regularization = 4 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.68182 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818 Epoch 5: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364 Epoch 25: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5896 - accuracy: 0.8636 - 34ms/epoch - 17ms/step ####################################################### the model mod153 use a learning rate = 0, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091 Epoch 5: val_accuracy did not improve from 0.84091 Epoch 6: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.9983 - accuracy: 0.7955 - 40ms/epoch - 20ms/step ####################################################### the model mod154 use a learning rate = 1, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.93182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.93182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.93182 Epoch 4: val_accuracy did not improve from 0.93182 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 1.1696 - accuracy: 0.8182 - 34ms/epoch - 17ms/step ####################################################### the model mod155 use a learning rate = 2, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.68182 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.90909 Epoch 8: val_accuracy did not improve from 0.90909 Epoch 9: val_accuracy did not improve from 0.90909 Epoch 10: val_accuracy did not improve from 0.90909 Epoch 11: val_accuracy did not improve from 0.90909 Epoch 12: val_accuracy did not improve from 0.90909 Epoch 13: val_accuracy did not improve from 0.90909 Epoch 14: val_accuracy did not improve from 0.90909 Epoch 15: val_accuracy did not improve from 0.90909 Epoch 16: val_accuracy did not improve from 0.90909 Epoch 17: val_accuracy did not improve from 0.90909 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3829 - accuracy: 0.8409 - 36ms/epoch - 18ms/step ####################################################### the model mod156 use a learning rate = 3, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.59091 Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091 Epoch 5: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.65909 Epoch 7: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.70455 Epoch 9: val_accuracy did not improve from 0.70455 Epoch 10: val_accuracy did not improve from 0.70455 Epoch 11: val_accuracy did not improve from 0.70455 Epoch 12: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.75000 Epoch 17: val_accuracy did not improve from 0.75000 Epoch 18: val_accuracy did not improve from 0.75000 Epoch 19: val_accuracy did not improve from 0.75000 Epoch 20: val_accuracy did not improve from 0.75000 Epoch 21: val_accuracy did not improve from 0.75000 Epoch 22: val_accuracy did not improve from 0.75000 Epoch 23: val_accuracy did not improve from 0.75000 Epoch 24: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.77273 Epoch 26: val_accuracy did not improve from 0.77273 Epoch 27: val_accuracy did not improve from 0.77273 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 34: val_accuracy did not improve from 0.79545 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy did not improve from 0.79545 Epoch 38: val_accuracy did not improve from 0.79545 Epoch 39: val_accuracy did not improve from 0.79545 Epoch 40: val_accuracy did not improve from 0.79545 Epoch 41: val_accuracy did not improve from 0.79545 Epoch 42: val_accuracy did not improve from 0.79545 Epoch 43: val_accuracy did not improve from 0.79545 Epoch 44: val_accuracy did not improve from 0.79545 Epoch 45: val_accuracy did not improve from 0.79545 Epoch 46: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 47: val_accuracy did not improve from 0.81818 Epoch 48: val_accuracy did not improve from 0.81818 Epoch 49: val_accuracy did not improve from 0.81818 Epoch 50: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 51: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 52: val_accuracy did not improve from 0.86364 Epoch 53: val_accuracy did not improve from 0.86364 Epoch 54: val_accuracy did not improve from 0.86364 Epoch 55: val_accuracy did not improve from 0.86364 Epoch 56: val_accuracy did not improve from 0.86364 Epoch 57: val_accuracy did not improve from 0.86364 Epoch 58: val_accuracy did not improve from 0.86364 Epoch 59: val_accuracy did not improve from 0.86364 Epoch 60: val_accuracy did not improve from 0.86364 Epoch 61: val_accuracy did not improve from 0.86364 Epoch 62: val_accuracy did not improve from 0.86364 Epoch 63: val_accuracy did not improve from 0.86364 Epoch 64: val_accuracy did not improve from 0.86364 Epoch 65: val_accuracy did not improve from 0.86364 Epoch 66: val_accuracy did not improve from 0.86364 Epoch 67: val_accuracy did not improve from 0.86364 Epoch 68: val_accuracy did not improve from 0.86364 Epoch 69: val_accuracy did not improve from 0.86364 Epoch 70: val_accuracy did not improve from 0.86364 Epoch 71: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.3465 - accuracy: 0.8636 - 35ms/epoch - 18ms/step ####################################################### the model mod157 use a learning rate = 4, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3189 - accuracy: 0.8864 - 39ms/epoch - 19ms/step ####################################################### the model mod158 use a learning rate = 5, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.70455 to 0.77273, saving model to best_model.h5 Epoch 5: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.90909 Epoch 19: val_accuracy did not improve from 0.90909 Epoch 20: val_accuracy did not improve from 0.90909 Epoch 21: val_accuracy did not improve from 0.90909 Epoch 22: val_accuracy did not improve from 0.90909 Epoch 23: val_accuracy did not improve from 0.90909 Epoch 24: val_accuracy did not improve from 0.90909 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3080 - accuracy: 0.9091 - 35ms/epoch - 17ms/step ####################################################### the model mod159 use a learning rate = 6, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.61364 Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364 Epoch 5: val_accuracy did not improve from 0.61364 Epoch 6: val_accuracy did not improve from 0.61364 Epoch 7: val_accuracy did not improve from 0.61364 Epoch 8: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.63636 Epoch 10: val_accuracy did not improve from 0.63636 Epoch 11: val_accuracy did not improve from 0.63636 Epoch 12: val_accuracy did not improve from 0.63636 Epoch 13: val_accuracy did not improve from 0.63636 Epoch 14: val_accuracy did not improve from 0.63636 Epoch 15: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.65909 Epoch 17: val_accuracy did not improve from 0.65909 Epoch 18: val_accuracy did not improve from 0.65909 Epoch 19: val_accuracy did not improve from 0.65909 Epoch 20: val_accuracy did not improve from 0.65909 Epoch 21: val_accuracy did not improve from 0.65909 Epoch 22: val_accuracy did not improve from 0.65909 Epoch 23: val_accuracy did not improve from 0.65909 Epoch 24: val_accuracy did not improve from 0.65909 Epoch 25: val_accuracy did not improve from 0.65909 Epoch 26: val_accuracy did not improve from 0.65909 Epoch 27: val_accuracy did not improve from 0.65909 Epoch 28: val_accuracy did not improve from 0.65909 Epoch 29: val_accuracy did not improve from 0.65909 Epoch 30: val_accuracy did not improve from 0.65909 Epoch 31: val_accuracy did not improve from 0.65909 Epoch 32: val_accuracy did not improve from 0.65909 Epoch 33: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 34: val_accuracy did not improve from 0.68182 Epoch 35: val_accuracy did not improve from 0.68182 Epoch 36: val_accuracy did not improve from 0.68182 Epoch 37: val_accuracy did not improve from 0.68182 Epoch 38: val_accuracy did not improve from 0.68182 Epoch 39: val_accuracy did not improve from 0.68182 Epoch 40: val_accuracy did not improve from 0.68182 Epoch 41: val_accuracy did not improve from 0.68182 Epoch 42: val_accuracy did not improve from 0.68182 Epoch 43: val_accuracy did not improve from 0.68182 Epoch 44: val_accuracy did not improve from 0.68182 Epoch 45: val_accuracy did not improve from 0.68182 Epoch 46: val_accuracy did not improve from 0.68182 Epoch 47: val_accuracy did not improve from 0.68182 Epoch 48: val_accuracy did not improve from 0.68182 Epoch 49: val_accuracy did not improve from 0.68182 Epoch 50: val_accuracy did not improve from 0.68182 Epoch 51: val_accuracy did not improve from 0.68182 Epoch 52: val_accuracy did not improve from 0.68182 Epoch 53: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 0.6591 - accuracy: 0.6818 - 34ms/epoch - 17ms/step ####################################################### the model mod160 use a learning rate = 7, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.38636 Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy did not improve from 0.38636 Epoch 7: val_accuracy did not improve from 0.38636 Epoch 8: val_accuracy did not improve from 0.38636 Epoch 9: val_accuracy did not improve from 0.38636 Epoch 10: val_accuracy did not improve from 0.38636 Epoch 11: val_accuracy did not improve from 0.38636 Epoch 12: val_accuracy did not improve from 0.38636 Epoch 13: val_accuracy did not improve from 0.38636 Epoch 14: val_accuracy did not improve from 0.38636 Epoch 15: val_accuracy did not improve from 0.38636 Epoch 16: val_accuracy did not improve from 0.38636 Epoch 17: val_accuracy did not improve from 0.38636 Epoch 18: val_accuracy did not improve from 0.38636 Epoch 19: val_accuracy did not improve from 0.38636 Epoch 20: val_accuracy did not improve from 0.38636 Epoch 21: val_accuracy did not improve from 0.38636
2/2 - 0s - loss: 1.1079 - accuracy: 0.3864 - 48ms/epoch - 24ms/step ####################################################### the model mod161 use a learning rate = 8, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.68182 Epoch 3: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.68182 Epoch 5: val_accuracy did not improve from 0.68182 Epoch 6: val_accuracy did not improve from 0.68182 Epoch 7: val_accuracy did not improve from 0.68182 Epoch 8: val_accuracy did not improve from 0.68182 Epoch 9: val_accuracy did not improve from 0.68182 Epoch 10: val_accuracy did not improve from 0.68182 Epoch 11: val_accuracy did not improve from 0.68182 Epoch 12: val_accuracy did not improve from 0.68182 Epoch 13: val_accuracy did not improve from 0.68182 Epoch 14: val_accuracy did not improve from 0.68182 Epoch 15: val_accuracy did not improve from 0.68182 Epoch 16: val_accuracy did not improve from 0.68182 Epoch 17: val_accuracy did not improve from 0.68182 Epoch 18: val_accuracy did not improve from 0.68182 Epoch 19: val_accuracy did not improve from 0.68182 Epoch 20: val_accuracy did not improve from 0.68182 Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 0.6617 - accuracy: 0.6818 - 34ms/epoch - 17ms/step ####################################################### the model mod162 use a learning rate = 9, l2 regularization = 5 and the optimizer = adam : Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.56818 Epoch 3: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.56818 Epoch 5: val_accuracy did not improve from 0.56818 Epoch 6: val_accuracy did not improve from 0.56818 Epoch 7: val_accuracy did not improve from 0.56818 Epoch 8: val_accuracy did not improve from 0.56818 Epoch 9: val_accuracy did not improve from 0.56818 Epoch 10: val_accuracy did not improve from 0.56818 Epoch 11: val_accuracy did not improve from 0.56818 Epoch 12: val_accuracy did not improve from 0.56818 Epoch 13: val_accuracy did not improve from 0.56818 Epoch 14: val_accuracy did not improve from 0.56818 Epoch 15: val_accuracy did not improve from 0.56818 Epoch 16: val_accuracy did not improve from 0.56818 Epoch 17: val_accuracy did not improve from 0.56818 Epoch 18: val_accuracy did not improve from 0.56818 Epoch 19: val_accuracy did not improve from 0.56818 Epoch 20: val_accuracy did not improve from 0.56818 Epoch 21: val_accuracy did not improve from 0.56818
2/2 - 0s - loss: 0.8059 - accuracy: 0.5682 - 34ms/epoch - 17ms/step ####################################################### the model mod163 use a learning rate = 0, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.88636 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.9870 - accuracy: 0.8409 - 38ms/epoch - 19ms/step ####################################################### the model mod164 use a learning rate = 1, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.72727 Epoch 4: val_accuracy did not improve from 0.72727 Epoch 5: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.75000 Epoch 7: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.77273 Epoch 9: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 10: val_accuracy did not improve from 0.79545 Epoch 11: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.81818 Epoch 13: val_accuracy did not improve from 0.81818 Epoch 14: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 15: val_accuracy did not improve from 0.84091 Epoch 16: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 17: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636 Epoch 25: val_accuracy did not improve from 0.88636 Epoch 26: val_accuracy did not improve from 0.88636 Epoch 27: val_accuracy did not improve from 0.88636 Epoch 28: val_accuracy did not improve from 0.88636 Epoch 29: val_accuracy did not improve from 0.88636 Epoch 30: val_accuracy did not improve from 0.88636 Epoch 31: val_accuracy did not improve from 0.88636 Epoch 32: val_accuracy did not improve from 0.88636 Epoch 33: val_accuracy did not improve from 0.88636 Epoch 34: val_accuracy did not improve from 0.88636 Epoch 35: val_accuracy did not improve from 0.88636 Epoch 36: val_accuracy did not improve from 0.88636 Epoch 37: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3758 - accuracy: 0.8636 - 36ms/epoch - 18ms/step ####################################################### the model mod165 use a learning rate = 2, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.22727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.22727 to 0.29545, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.29545 to 0.36364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy improved from 0.38636 to 0.45455, saving model to best_model.h5 Epoch 7: val_accuracy improved from 0.45455 to 0.50000, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.50000 Epoch 9: val_accuracy improved from 0.50000 to 0.54545, saving model to best_model.h5 Epoch 10: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 11: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5 Epoch 12: val_accuracy did not improve from 0.61364 Epoch 13: val_accuracy did not improve from 0.61364 Epoch 14: val_accuracy did not improve from 0.61364 Epoch 15: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.63636 Epoch 17: val_accuracy did not improve from 0.63636 Epoch 18: val_accuracy did not improve from 0.63636 Epoch 19: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5 Epoch 20: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.70455 Epoch 22: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 23: val_accuracy did not improve from 0.72727 Epoch 24: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.75000 Epoch 26: val_accuracy did not improve from 0.75000 Epoch 27: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 28: val_accuracy did not improve from 0.77273 Epoch 29: val_accuracy did not improve from 0.77273 Epoch 30: val_accuracy did not improve from 0.77273 Epoch 31: val_accuracy did not improve from 0.77273 Epoch 32: val_accuracy did not improve from 0.77273 Epoch 33: val_accuracy did not improve from 0.77273 Epoch 34: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.79545 Epoch 36: val_accuracy did not improve from 0.79545 Epoch 37: val_accuracy did not improve from 0.79545 Epoch 38: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 39: val_accuracy did not improve from 0.81818 Epoch 40: val_accuracy did not improve from 0.81818 Epoch 41: val_accuracy did not improve from 0.81818 Epoch 42: val_accuracy did not improve from 0.81818 Epoch 43: val_accuracy did not improve from 0.81818 Epoch 44: val_accuracy did not improve from 0.81818 Epoch 45: val_accuracy did not improve from 0.81818 Epoch 46: val_accuracy did not improve from 0.81818 Epoch 47: val_accuracy did not improve from 0.81818 Epoch 48: val_accuracy did not improve from 0.81818 Epoch 49: val_accuracy did not improve from 0.81818 Epoch 50: val_accuracy did not improve from 0.81818 Epoch 51: val_accuracy did not improve from 0.81818 Epoch 52: val_accuracy did not improve from 0.81818 Epoch 53: val_accuracy did not improve from 0.81818 Epoch 54: val_accuracy did not improve from 0.81818 Epoch 55: val_accuracy did not improve from 0.81818 Epoch 56: val_accuracy did not improve from 0.81818 Epoch 57: val_accuracy did not improve from 0.81818 Epoch 58: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.4457 - accuracy: 0.8182 - 48ms/epoch - 24ms/step ####################################################### the model mod166 use a learning rate = 3, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.50000 Epoch 3: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.50000 Epoch 5: val_accuracy did not improve from 0.50000 Epoch 6: val_accuracy did not improve from 0.50000 Epoch 7: val_accuracy did not improve from 0.50000 Epoch 8: val_accuracy did not improve from 0.50000 Epoch 9: val_accuracy did not improve from 0.50000 Epoch 10: val_accuracy did not improve from 0.50000 Epoch 11: val_accuracy did not improve from 0.50000 Epoch 12: val_accuracy did not improve from 0.50000 Epoch 13: val_accuracy did not improve from 0.50000 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy did not improve from 0.50000 Epoch 16: val_accuracy did not improve from 0.50000 Epoch 17: val_accuracy did not improve from 0.50000 Epoch 18: val_accuracy did not improve from 0.50000 Epoch 19: val_accuracy did not improve from 0.50000 Epoch 20: val_accuracy did not improve from 0.50000 Epoch 21: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 0.9275 - accuracy: 0.5000 - 39ms/epoch - 19ms/step ####################################################### the model mod167 use a learning rate = 4, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy did not improve from 0.38636 Epoch 6: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.40909 Epoch 8: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 9: val_accuracy did not improve from 0.43182 Epoch 10: val_accuracy did not improve from 0.43182 Epoch 11: val_accuracy did not improve from 0.43182 Epoch 12: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 13: val_accuracy improved from 0.45455 to 0.50000, saving model to best_model.h5 Epoch 14: val_accuracy did not improve from 0.50000 Epoch 15: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 16: val_accuracy did not improve from 0.52273 Epoch 17: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5 Epoch 18: val_accuracy did not improve from 0.54545 Epoch 19: val_accuracy did not improve from 0.54545 Epoch 20: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5 Epoch 21: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 22: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5 Epoch 23: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5 Epoch 24: val_accuracy did not improve from 0.65909 Epoch 25: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5 Epoch 26: val_accuracy did not improve from 0.68182 Epoch 27: val_accuracy did not improve from 0.68182 Epoch 28: val_accuracy did not improve from 0.68182 Epoch 29: val_accuracy did not improve from 0.68182 Epoch 30: val_accuracy did not improve from 0.68182 Epoch 31: val_accuracy did not improve from 0.68182 Epoch 32: val_accuracy did not improve from 0.68182 Epoch 33: val_accuracy did not improve from 0.68182 Epoch 34: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5 Epoch 35: val_accuracy did not improve from 0.70455 Epoch 36: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5 Epoch 37: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5 Epoch 38: val_accuracy did not improve from 0.75000 Epoch 39: val_accuracy did not improve from 0.75000 Epoch 40: val_accuracy did not improve from 0.75000 Epoch 41: val_accuracy did not improve from 0.75000 Epoch 42: val_accuracy did not improve from 0.75000 Epoch 43: val_accuracy did not improve from 0.75000 Epoch 44: val_accuracy did not improve from 0.75000 Epoch 45: val_accuracy did not improve from 0.75000 Epoch 46: val_accuracy did not improve from 0.75000 Epoch 47: val_accuracy did not improve from 0.75000 Epoch 48: val_accuracy did not improve from 0.75000 Epoch 49: val_accuracy did not improve from 0.75000 Epoch 50: val_accuracy did not improve from 0.75000 Epoch 51: val_accuracy did not improve from 0.75000 Epoch 52: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5 Epoch 53: val_accuracy did not improve from 0.77273 Epoch 54: val_accuracy did not improve from 0.77273 Epoch 55: val_accuracy did not improve from 0.77273 Epoch 56: val_accuracy did not improve from 0.77273 Epoch 57: val_accuracy did not improve from 0.77273 Epoch 58: val_accuracy did not improve from 0.77273 Epoch 59: val_accuracy did not improve from 0.77273 Epoch 60: val_accuracy did not improve from 0.77273 Epoch 61: val_accuracy did not improve from 0.77273 Epoch 62: val_accuracy did not improve from 0.77273 Epoch 63: val_accuracy did not improve from 0.77273 Epoch 64: val_accuracy did not improve from 0.77273 Epoch 65: val_accuracy did not improve from 0.77273 Epoch 66: val_accuracy did not improve from 0.77273 Epoch 67: val_accuracy did not improve from 0.77273 Epoch 68: val_accuracy did not improve from 0.77273 Epoch 69: val_accuracy did not improve from 0.77273 Epoch 70: val_accuracy did not improve from 0.77273 Epoch 71: val_accuracy did not improve from 0.77273 Epoch 72: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5593 - accuracy: 0.7727 - 41ms/epoch - 20ms/step ####################################################### the model mod168 use a learning rate = 5, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.34091 Epoch 3: val_accuracy improved from 0.34091 to 0.38636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636 Epoch 5: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5 Epoch 6: val_accuracy did not improve from 0.40909 Epoch 7: val_accuracy did not improve from 0.40909 Epoch 8: val_accuracy did not improve from 0.40909 Epoch 9: val_accuracy did not improve from 0.40909 Epoch 10: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.43182 Epoch 12: val_accuracy did not improve from 0.43182 Epoch 13: val_accuracy did not improve from 0.43182 Epoch 14: val_accuracy did not improve from 0.43182 Epoch 15: val_accuracy did not improve from 0.43182 Epoch 16: val_accuracy did not improve from 0.43182 Epoch 17: val_accuracy did not improve from 0.43182 Epoch 18: val_accuracy did not improve from 0.43182 Epoch 19: val_accuracy did not improve from 0.43182 Epoch 20: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5 Epoch 21: val_accuracy did not improve from 0.45455 Epoch 22: val_accuracy did not improve from 0.45455 Epoch 23: val_accuracy did not improve from 0.45455 Epoch 24: val_accuracy did not improve from 0.45455 Epoch 25: val_accuracy did not improve from 0.45455 Epoch 26: val_accuracy did not improve from 0.45455 Epoch 27: val_accuracy did not improve from 0.45455 Epoch 28: val_accuracy did not improve from 0.45455 Epoch 29: val_accuracy did not improve from 0.45455 Epoch 30: val_accuracy did not improve from 0.45455 Epoch 31: val_accuracy did not improve from 0.45455 Epoch 32: val_accuracy did not improve from 0.45455 Epoch 33: val_accuracy did not improve from 0.45455 Epoch 34: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5 Epoch 35: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5 Epoch 36: val_accuracy did not improve from 0.50000 Epoch 37: val_accuracy did not improve from 0.50000 Epoch 38: val_accuracy did not improve from 0.50000 Epoch 39: val_accuracy did not improve from 0.50000 Epoch 40: val_accuracy did not improve from 0.50000 Epoch 41: val_accuracy did not improve from 0.50000 Epoch 42: val_accuracy did not improve from 0.50000 Epoch 43: val_accuracy did not improve from 0.50000 Epoch 44: val_accuracy did not improve from 0.50000 Epoch 45: val_accuracy did not improve from 0.50000 Epoch 46: val_accuracy did not improve from 0.50000 Epoch 47: val_accuracy did not improve from 0.50000 Epoch 48: val_accuracy did not improve from 0.50000 Epoch 49: val_accuracy did not improve from 0.50000 Epoch 50: val_accuracy did not improve from 0.50000 Epoch 51: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5 Epoch 52: val_accuracy did not improve from 0.52273 Epoch 53: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5 Epoch 54: val_accuracy did not improve from 0.56818 Epoch 55: val_accuracy did not improve from 0.56818 Epoch 56: val_accuracy did not improve from 0.56818 Epoch 57: val_accuracy did not improve from 0.56818 Epoch 58: val_accuracy did not improve from 0.56818 Epoch 59: val_accuracy did not improve from 0.56818 Epoch 60: val_accuracy did not improve from 0.56818 Epoch 61: val_accuracy did not improve from 0.56818 Epoch 62: val_accuracy did not improve from 0.56818 Epoch 63: val_accuracy did not improve from 0.56818 Epoch 64: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5 Epoch 65: val_accuracy did not improve from 0.59091 Epoch 66: val_accuracy did not improve from 0.59091 Epoch 67: val_accuracy did not improve from 0.59091 Epoch 68: val_accuracy did not improve from 0.59091 Epoch 69: val_accuracy did not improve from 0.59091 Epoch 70: val_accuracy did not improve from 0.59091 Epoch 71: val_accuracy did not improve from 0.59091 Epoch 72: val_accuracy did not improve from 0.59091 Epoch 73: val_accuracy did not improve from 0.59091 Epoch 74: val_accuracy did not improve from 0.59091 Epoch 75: val_accuracy did not improve from 0.59091 Epoch 76: val_accuracy did not improve from 0.59091 Epoch 77: val_accuracy did not improve from 0.59091 Epoch 78: val_accuracy did not improve from 0.59091 Epoch 79: val_accuracy did not improve from 0.59091 Epoch 80: val_accuracy did not improve from 0.59091 Epoch 81: val_accuracy did not improve from 0.59091 Epoch 82: val_accuracy did not improve from 0.59091 Epoch 83: val_accuracy did not improve from 0.59091 Epoch 84: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 0.7157 - accuracy: 0.5909 - 72ms/epoch - 36ms/step ####################################################### the model mod169 use a learning rate = 6, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.72727 Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727 Epoch 5: val_accuracy did not improve from 0.72727 Epoch 6: val_accuracy did not improve from 0.72727 Epoch 7: val_accuracy did not improve from 0.72727 Epoch 8: val_accuracy did not improve from 0.72727 Epoch 9: val_accuracy did not improve from 0.72727 Epoch 10: val_accuracy did not improve from 0.72727 Epoch 11: val_accuracy did not improve from 0.72727 Epoch 12: val_accuracy did not improve from 0.72727 Epoch 13: val_accuracy did not improve from 0.72727 Epoch 14: val_accuracy did not improve from 0.72727 Epoch 15: val_accuracy did not improve from 0.72727 Epoch 16: val_accuracy did not improve from 0.72727 Epoch 17: val_accuracy did not improve from 0.72727 Epoch 18: val_accuracy did not improve from 0.72727 Epoch 19: val_accuracy did not improve from 0.72727 Epoch 20: val_accuracy did not improve from 0.72727 Epoch 21: val_accuracy did not improve from 0.72727
2/2 - 0s - loss: 0.7427 - accuracy: 0.7273 - 35ms/epoch - 17ms/step ####################################################### the model mod170 use a learning rate = 7, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.31818, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.31818 Epoch 3: val_accuracy did not improve from 0.31818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.31818 Epoch 5: val_accuracy did not improve from 0.31818 Epoch 6: val_accuracy did not improve from 0.31818 Epoch 7: val_accuracy did not improve from 0.31818 Epoch 8: val_accuracy did not improve from 0.31818 Epoch 9: val_accuracy did not improve from 0.31818 Epoch 10: val_accuracy did not improve from 0.31818 Epoch 11: val_accuracy did not improve from 0.31818 Epoch 12: val_accuracy did not improve from 0.31818 Epoch 13: val_accuracy did not improve from 0.31818 Epoch 14: val_accuracy did not improve from 0.31818 Epoch 15: val_accuracy did not improve from 0.31818 Epoch 16: val_accuracy did not improve from 0.31818 Epoch 17: val_accuracy did not improve from 0.31818 Epoch 18: val_accuracy did not improve from 0.31818 Epoch 19: val_accuracy did not improve from 0.31818 Epoch 20: val_accuracy did not improve from 0.31818 Epoch 21: val_accuracy did not improve from 0.31818
2/2 - 0s - loss: 0.9423 - accuracy: 0.3182 - 36ms/epoch - 18ms/step ####################################################### the model mod171 use a learning rate = 8, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.36364 Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364 Epoch 5: val_accuracy did not improve from 0.36364 Epoch 6: val_accuracy did not improve from 0.36364 Epoch 7: val_accuracy did not improve from 0.36364 Epoch 8: val_accuracy did not improve from 0.36364 Epoch 9: val_accuracy did not improve from 0.36364 Epoch 10: val_accuracy did not improve from 0.36364 Epoch 11: val_accuracy did not improve from 0.36364 Epoch 12: val_accuracy did not improve from 0.36364 Epoch 13: val_accuracy did not improve from 0.36364 Epoch 14: val_accuracy did not improve from 0.36364 Epoch 15: val_accuracy did not improve from 0.36364 Epoch 16: val_accuracy did not improve from 0.36364 Epoch 17: val_accuracy did not improve from 0.36364 Epoch 18: val_accuracy did not improve from 0.36364 Epoch 19: val_accuracy did not improve from 0.36364 Epoch 20: val_accuracy did not improve from 0.36364 Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.8387 - accuracy: 0.3636 - 39ms/epoch - 20ms/step ####################################################### the model mod172 use a learning rate = 9, l2 regularization = 5 and the optimizer = adagrad : Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.61364 Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364 Epoch 5: val_accuracy did not improve from 0.61364 Epoch 6: val_accuracy did not improve from 0.61364 Epoch 7: val_accuracy did not improve from 0.61364 Epoch 8: val_accuracy did not improve from 0.61364 Epoch 9: val_accuracy did not improve from 0.61364 Epoch 10: val_accuracy did not improve from 0.61364 Epoch 11: val_accuracy did not improve from 0.61364 Epoch 12: val_accuracy did not improve from 0.61364 Epoch 13: val_accuracy did not improve from 0.61364 Epoch 14: val_accuracy did not improve from 0.61364 Epoch 15: val_accuracy did not improve from 0.61364 Epoch 16: val_accuracy did not improve from 0.61364 Epoch 17: val_accuracy did not improve from 0.61364 Epoch 18: val_accuracy did not improve from 0.61364 Epoch 19: val_accuracy did not improve from 0.61364 Epoch 20: val_accuracy did not improve from 0.61364 Epoch 21: val_accuracy did not improve from 0.61364
2/2 - 0s - loss: 0.6587 - accuracy: 0.6136 - 63ms/epoch - 32ms/step ####################################################### the model mod173 use a learning rate = 0, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5719 - accuracy: 0.8182 - 40ms/epoch - 20ms/step ####################################################### the model mod174 use a learning rate = 1, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5637 - accuracy: 0.8409 - 34ms/epoch - 17ms/step ####################################################### the model mod175 use a learning rate = 2, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5411 - accuracy: 0.8182 - 48ms/epoch - 24ms/step ####################################################### the model mod176 use a learning rate = 3, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5 Epoch 4: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5174 - accuracy: 0.8409 - 39ms/epoch - 19ms/step ####################################################### the model mod177 use a learning rate = 4, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182 Epoch 25: val_accuracy did not improve from 0.93182 Epoch 26: val_accuracy did not improve from 0.93182 Epoch 27: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5171 - accuracy: 0.8409 - 34ms/epoch - 17ms/step ####################################################### the model mod178 use a learning rate = 5, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.75000 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.88636 Epoch 6: val_accuracy did not improve from 0.88636 Epoch 7: val_accuracy did not improve from 0.88636 Epoch 8: val_accuracy did not improve from 0.88636 Epoch 9: val_accuracy did not improve from 0.88636 Epoch 10: val_accuracy did not improve from 0.88636 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6274 - accuracy: 0.7955 - 66ms/epoch - 33ms/step ####################################################### the model mod179 use a learning rate = 6, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.70455 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545 Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.81818 Epoch 6: val_accuracy did not improve from 0.81818 Epoch 7: val_accuracy did not improve from 0.81818 Epoch 8: val_accuracy did not improve from 0.81818 Epoch 9: val_accuracy did not improve from 0.81818 Epoch 10: val_accuracy did not improve from 0.81818 Epoch 11: val_accuracy did not improve from 0.81818 Epoch 12: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364 Epoch 25: val_accuracy did not improve from 0.86364 Epoch 26: val_accuracy did not improve from 0.86364 Epoch 27: val_accuracy did not improve from 0.86364 Epoch 28: val_accuracy did not improve from 0.86364 Epoch 29: val_accuracy did not improve from 0.86364 Epoch 30: val_accuracy did not improve from 0.86364 Epoch 31: val_accuracy did not improve from 0.86364 Epoch 32: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5747 - accuracy: 0.7727 - 44ms/epoch - 22ms/step ####################################################### the model mod180 use a learning rate = 7, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364 Epoch 5: val_accuracy did not improve from 0.86364 Epoch 6: val_accuracy did not improve from 0.86364 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5834 - accuracy: 0.8182 - 40ms/epoch - 20ms/step ####################################################### the model mod181 use a learning rate = 8, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5 Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818 Epoch 5: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5 Epoch 6: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5 Epoch 7: val_accuracy did not improve from 0.86364 Epoch 8: val_accuracy did not improve from 0.86364 Epoch 9: val_accuracy did not improve from 0.86364 Epoch 10: val_accuracy did not improve from 0.86364 Epoch 11: val_accuracy did not improve from 0.86364 Epoch 12: val_accuracy did not improve from 0.86364 Epoch 13: val_accuracy did not improve from 0.86364 Epoch 14: val_accuracy did not improve from 0.86364 Epoch 15: val_accuracy did not improve from 0.86364 Epoch 16: val_accuracy did not improve from 0.86364 Epoch 17: val_accuracy did not improve from 0.86364 Epoch 18: val_accuracy did not improve from 0.86364 Epoch 19: val_accuracy did not improve from 0.86364 Epoch 20: val_accuracy did not improve from 0.86364 Epoch 21: val_accuracy did not improve from 0.86364 Epoch 22: val_accuracy did not improve from 0.86364 Epoch 23: val_accuracy did not improve from 0.86364 Epoch 24: val_accuracy did not improve from 0.86364 Epoch 25: val_accuracy did not improve from 0.86364 Epoch 26: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.7406 - accuracy: 0.7727 - 43ms/epoch - 21ms/step ####################################################### the model mod182 use a learning rate = 9, l2 regularization = 5 and the optimizer = SGD : Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5 Epoch 2: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5 Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 4: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5 Epoch 5: val_accuracy did not improve from 0.93182 Epoch 6: val_accuracy did not improve from 0.93182 Epoch 7: val_accuracy did not improve from 0.93182 Epoch 8: val_accuracy did not improve from 0.93182 Epoch 9: val_accuracy did not improve from 0.93182 Epoch 10: val_accuracy did not improve from 0.93182 Epoch 11: val_accuracy did not improve from 0.93182 Epoch 12: val_accuracy did not improve from 0.93182 Epoch 13: val_accuracy did not improve from 0.93182 Epoch 14: val_accuracy did not improve from 0.93182 Epoch 15: val_accuracy did not improve from 0.93182 Epoch 16: val_accuracy did not improve from 0.93182 Epoch 17: val_accuracy did not improve from 0.93182 Epoch 18: val_accuracy did not improve from 0.93182 Epoch 19: val_accuracy did not improve from 0.93182 Epoch 20: val_accuracy did not improve from 0.93182 Epoch 21: val_accuracy did not improve from 0.93182 Epoch 22: val_accuracy did not improve from 0.93182 Epoch 23: val_accuracy did not improve from 0.93182 Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5378 - accuracy: 0.8636 - 37ms/epoch - 18ms/step
classement=sorted(classement, reverse = True)
print(str(i+1)+"-" + str(classement[i][1])+" with an accuracy of :"+ str(round(classement[i][0]*100,3)))
1-mod82 with an accuracy of :90.909 2-mod60 with an accuracy of :90.909 3-mod5 with an accuracy of :90.909 4-mod28 with an accuracy of :90.909 5-mod158 with an accuracy of :90.909 6-mod104 with an accuracy of :90.909 7-mod97 with an accuracy of :88.636 8-mod87 with an accuracy of :88.636 9-mod84 with an accuracy of :88.636 10-mod8 with an accuracy of :88.636 11-mod67 with an accuracy of :88.636 12-mod56 with an accuracy of :88.636 13-mod55 with an accuracy of :88.636 14-mod44 with an accuracy of :88.636 15-mod43 with an accuracy of :88.636 16-mod38 with an accuracy of :88.636 17-mod37 with an accuracy of :88.636 18-mod36 with an accuracy of :88.636 19-mod35 with an accuracy of :88.636 20-mod25 with an accuracy of :88.636 21-mod157 with an accuracy of :88.636 22-mod14 with an accuracy of :88.636 23-mod127 with an accuracy of :88.636 24-mod119 with an accuracy of :88.636 25-mod92 with an accuracy of :86.364 26-mod90 with an accuracy of :86.364 27-mod68 with an accuracy of :86.364 28-mod65 with an accuracy of :86.364 29-mod53 with an accuracy of :86.364 30-mod45 with an accuracy of :86.364 31-mod4 with an accuracy of :86.364 32-mod26 with an accuracy of :86.364 33-mod182 with an accuracy of :86.364 34-mod164 with an accuracy of :86.364 35-mod156 with an accuracy of :86.364 36-mod152 with an accuracy of :86.364 37-mod15 with an accuracy of :86.364 38-mod147 with an accuracy of :86.364 39-mod137 with an accuracy of :86.364 40-mod126 with an accuracy of :86.364 41-mod125 with an accuracy of :86.364 42-mod120 with an accuracy of :86.364 43-mod96 with an accuracy of :84.091 44-mod95 with an accuracy of :84.091 45-mod91 with an accuracy of :84.091 46-mod88 with an accuracy of :84.091 47-mod85 with an accuracy of :84.091 48-mod83 with an accuracy of :84.091 49-mod79 with an accuracy of :84.091 50-mod74 with an accuracy of :84.091 51-mod62 with an accuracy of :84.091 52-mod57 with an accuracy of :84.091 53-mod31 with an accuracy of :84.091 54-mod177 with an accuracy of :84.091 55-mod176 with an accuracy of :84.091 56-mod174 with an accuracy of :84.091 57-mod163 with an accuracy of :84.091 58-mod155 with an accuracy of :84.091 59-mod149 with an accuracy of :84.091 60-mod135 with an accuracy of :84.091 61-mod128 with an accuracy of :84.091 62-mod118 with an accuracy of :84.091 63-mod98 with an accuracy of :81.818 64-mod94 with an accuracy of :81.818 65-mod89 with an accuracy of :81.818 66-mod86 with an accuracy of :81.818 67-mod75 with an accuracy of :81.818 68-mod64 with an accuracy of :81.818 69-mod61 with an accuracy of :81.818 70-mod59 with an accuracy of :81.818 71-mod58 with an accuracy of :81.818 72-mod54 with an accuracy of :81.818 73-mod34 with an accuracy of :81.818 74-mod32 with an accuracy of :81.818 75-mod30 with an accuracy of :81.818 76-mod27 with an accuracy of :81.818 77-mod24 with an accuracy of :81.818 78-mod180 with an accuracy of :81.818 79-mod175 with an accuracy of :81.818 80-mod173 with an accuracy of :81.818 81-mod17 with an accuracy of :81.818 82-mod165 with an accuracy of :81.818 83-mod154 with an accuracy of :81.818 84-mod151 with an accuracy of :81.818 85-mod148 with an accuracy of :81.818 86-mod144 with an accuracy of :81.818 87-mod116 with an accuracy of :81.818 88-mod115 with an accuracy of :81.818 89-mod114 with an accuracy of :81.818 90-mod77 with an accuracy of :79.545 91-mod73 with an accuracy of :79.545 92-mod66 with an accuracy of :79.545 93-mod6 with an accuracy of :79.545 94-mod29 with an accuracy of :79.545 95-mod178 with an accuracy of :79.545 96-mod153 with an accuracy of :79.545 97-mod150 with an accuracy of :79.545 98-mod146 with an accuracy of :79.545 99-mod134 with an accuracy of :79.545 100-mod124 with an accuracy of :79.545 101-mod122 with an accuracy of :79.545 102-mod113 with an accuracy of :79.545 103-mod105 with an accuracy of :79.545 104-mod78 with an accuracy of :77.273 105-mod72 with an accuracy of :77.273 106-mod7 with an accuracy of :77.273 107-mod181 with an accuracy of :77.273 108-mod179 with an accuracy of :77.273 109-mod167 with an accuracy of :77.273 110-mod145 with an accuracy of :77.273 111-mod143 with an accuracy of :77.273 112-mod117 with an accuracy of :77.273 113-mod107 with an accuracy of :77.273 114-mod70 with an accuracy of :75.0 115-mod51 with an accuracy of :75.0 116-mod121 with an accuracy of :75.0 117-mod108 with an accuracy of :75.0 118-mod106 with an accuracy of :75.0 119-mod103 with an accuracy of :75.0 120-mod102 with an accuracy of :75.0 121-mod9 with an accuracy of :72.727 122-mod169 with an accuracy of :72.727 123-mod138 with an accuracy of :72.727 124-mod133 with an accuracy of :72.727 125-mod47 with an accuracy of :70.455 126-mod130 with an accuracy of :70.455 127-mod10 with an accuracy of :70.455 128-mod48 with an accuracy of :68.182 129-mod161 with an accuracy of :68.182 130-mod16 with an accuracy of :68.182 131-mod159 with an accuracy of :68.182 132-mod123 with an accuracy of :68.182 133-mod11 with an accuracy of :68.182 134-mod69 with an accuracy of :65.909 135-mod131 with an accuracy of :65.909 136-mod129 with an accuracy of :65.909 137-mod101 with an accuracy of :65.909 138-mod23 with an accuracy of :63.636 139-mod112 with an accuracy of :63.636 140-mod93 with an accuracy of :61.364 141-mod49 with an accuracy of :61.364 142-mod172 with an accuracy of :61.364 143-mod76 with an accuracy of :59.091 144-mod168 with an accuracy of :59.091 145-mod100 with an accuracy of :59.091 146-mod20 with an accuracy of :56.818 147-mod162 with an accuracy of :56.818 148-mod80 with an accuracy of :54.545 149-mod63 with an accuracy of :54.545 150-mod46 with an accuracy of :54.545 151-mod33 with an accuracy of :54.545 152-mod3 with an accuracy of :54.545 153-mod22 with an accuracy of :54.545 154-mod19 with an accuracy of :54.545 155-mod41 with an accuracy of :52.273 156-mod141 with an accuracy of :52.273 157-mod52 with an accuracy of :50.0 158-mod18 with an accuracy of :50.0 159-mod166 with an accuracy of :50.0 160-mod139 with an accuracy of :50.0 161-mod13 with an accuracy of :50.0 162-mod81 with an accuracy of :47.727 163-mod12 with an accuracy of :47.727 164-mod111 with an accuracy of :45.455 165-mod99 with an accuracy of :43.182 166-mod40 with an accuracy of :40.909 167-mod136 with an accuracy of :40.909 168-mod109 with an accuracy of :40.909 169-mod160 with an accuracy of :38.636 170-mod21 with an accuracy of :36.364 171-mod171 with an accuracy of :36.364 172-mod142 with an accuracy of :36.364 173-mod140 with an accuracy of :36.364 174-mod110 with an accuracy of :34.091 175-mod50 with an accuracy of :31.818 176-mod170 with an accuracy of :31.818 177-mod132 with an accuracy of :29.545 178-mod71 with an accuracy of :27.273 179-mod42 with an accuracy of :22.727 180-mod39 with an accuracy of :18.182
Now that we tested all the possible models, we will select the most performant model that doesn't overfit.
When we look at the learning curve
with this analysis we will use the model mod104 because the curves of the learning rate of the train and the test are closer in general and the performance are better.
model = keras.Sequential([
keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform',kernel_regularizer=keras.regularizers.l2(3)),
#13 neurons because it's the number of columns/inputs
keras.layers.Dense(2, activation='sigmoid')
#2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
opt=Adagrad(learning_rate= 1)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0, callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5 Epoch 2: val_accuracy did not improve from 0.68182 Epoch 3: val_accuracy did not improve from 0.68182 Epoch 4: val_accuracy improved from 0.68182 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.79545 Epoch 6: val_accuracy did not improve from 0.79545 Epoch 7: val_accuracy did not improve from 0.79545 Epoch 8: val_accuracy did not improve from 0.79545 Epoch 9: val_accuracy did not improve from 0.79545 Epoch 10: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5 Epoch 11: val_accuracy did not improve from 0.88636 Epoch 12: val_accuracy did not improve from 0.88636 Epoch 13: val_accuracy did not improve from 0.88636 Epoch 14: val_accuracy did not improve from 0.88636 Epoch 15: val_accuracy did not improve from 0.88636 Epoch 16: val_accuracy did not improve from 0.88636 Epoch 17: val_accuracy did not improve from 0.88636 Epoch 18: val_accuracy did not improve from 0.88636 Epoch 19: val_accuracy did not improve from 0.88636 Epoch 20: val_accuracy did not improve from 0.88636 Epoch 21: val_accuracy did not improve from 0.88636 Epoch 22: val_accuracy did not improve from 0.88636 Epoch 23: val_accuracy did not improve from 0.88636 Epoch 24: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5 Epoch 25: val_accuracy did not improve from 0.90909 Epoch 26: val_accuracy did not improve from 0.90909 Epoch 27: val_accuracy did not improve from 0.90909 Epoch 28: val_accuracy did not improve from 0.90909 Epoch 29: val_accuracy did not improve from 0.90909 Epoch 30: val_accuracy did not improve from 0.90909 Epoch 31: val_accuracy did not improve from 0.90909 Epoch 32: val_accuracy did not improve from 0.90909 Epoch 33: val_accuracy did not improve from 0.90909 Epoch 34: val_accuracy did not improve from 0.90909 Epoch 35: val_accuracy did not improve from 0.90909 Epoch 36: val_accuracy did not improve from 0.90909 Epoch 37: val_accuracy did not improve from 0.90909 Epoch 38: val_accuracy did not improve from 0.90909 Epoch 39: val_accuracy did not improve from 0.90909 Epoch 40: val_accuracy did not improve from 0.90909 Epoch 41: val_accuracy did not improve from 0.90909 Epoch 42: val_accuracy did not improve from 0.90909 Epoch 43: val_accuracy did not improve from 0.90909 Epoch 44: val_accuracy did not improve from 0.90909
X = small_validation.drop(columns="disease")
y= small_validation["disease"]
X-= mean
X /= std
small_predictions = model.predict(X)
result =[]
for i in range(len(small_predictions)):
if small_predictions[i][0]>small_predictions[i][1]:
result.append(1)#the real class
else :
result.append(2)#the real class
score = accuracy_score(small_validation["disease"], result)
print("accuracy : "+str(score))
2/2 [==============================] - 0s 8ms/step accuracy : 0.7777777777777778
#writing results for exam evaluation
r= open("results_small_dataset.txt", "w+")
r.write(str(result))
r.close()